avfilter: add arnndn filter

4 files changed, 1548 insertions, 2 deletions

diff --git a/libavfilter/Makefile b/libavfilter/Makefile
index 16bb8cd965..e9ac54daee 100644
--- a/libavfilter/Makefile
+++ b/libavfilter/Makefile
@@ -72,6 +72,7 @@ OBJS-$(CONFIG_APULSATOR_FILTER)              += af_apulsator.o
 OBJS-$(CONFIG_AREALTIME_FILTER)              += f_realtime.o
 OBJS-$(CONFIG_ARESAMPLE_FILTER)              += af_aresample.o
 OBJS-$(CONFIG_AREVERSE_FILTER)               += f_reverse.o
+OBJS-$(CONFIG_ARNNDN_FILTER)                 += af_arnndn.o
 OBJS-$(CONFIG_ASELECT_FILTER)                += f_select.o
 OBJS-$(CONFIG_ASENDCMD_FILTER)               += f_sendcmd.o
 OBJS-$(CONFIG_ASETNSAMPLES_FILTER)           += af_asetnsamples.o
diff --git a/libavfilter/af_arnndn.c b/libavfilter/af_arnndn.c
new file mode 100644
index 0000000000..781d0dc934
--- /dev/null
+++ b/libavfilter/af_arnndn.c
@@ -0,0 +1,1544 @@
+/*
+ * Copyright (c) 2018 Gregor Richards
+ * Copyright (c) 2017 Mozilla
+ * Copyright (c) 2005-2009 Xiph.Org Foundation
+ * Copyright (c) 2007-2008 CSIRO
+ * Copyright (c) 2008-2011 Octasic Inc.
+ * Copyright (c) Jean-Marc Valin
+ * Copyright (c) 2019 Paul B Mahol
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * - Redistributions of source code must retain the above copyright
+ *   notice, this list of conditions and the following disclaimer.
+ *
+ * - Redistributions in binary form must reproduce the above copyright
+ *   notice, this list of conditions and the following disclaimer in the
+ *   documentation and/or other materials provided with the distribution.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+ * ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+ * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+ * A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
+ * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+ * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+ * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+ * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+ * LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+ * NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ */
+
+#include <float.h>
+
+#include "libavutil/avassert.h"
+#include "libavutil/avstring.h"
+#include "libavutil/float_dsp.h"
+#include "libavutil/opt.h"
+#include "libavutil/tx.h"
+#include "avfilter.h"
+#include "audio.h"
+#include "filters.h"
+#include "formats.h"
+
+#define FRAME_SIZE_SHIFT 2
+#define FRAME_SIZE (120<<FRAME_SIZE_SHIFT)
+#define WINDOW_SIZE (2*FRAME_SIZE)
+#define FREQ_SIZE (FRAME_SIZE + 1)
+
+#define PITCH_MIN_PERIOD 60
+#define PITCH_MAX_PERIOD 768
+#define PITCH_FRAME_SIZE 960
+#define PITCH_BUF_SIZE (PITCH_MAX_PERIOD+PITCH_FRAME_SIZE)
+
+#define SQUARE(x) ((x)*(x))
+
+#define NB_BANDS 22
+
+#define CEPS_MEM 8
+#define NB_DELTA_CEPS 6
+
+#define NB_FEATURES (NB_BANDS+3*NB_DELTA_CEPS+2)
+
+#define WEIGHTS_SCALE (1.f/256)
+
+#define MAX_NEURONS 128
+
+#define ACTIVATION_TANH    0
+#define ACTIVATION_SIGMOID 1
+#define ACTIVATION_RELU    2
+
+#define Q15ONE 1.0f
+
+typedef struct DenseLayer {
+    const float *bias;
+    const float *input_weights;
+    int nb_inputs;
+    int nb_neurons;
+    int activation;
+} DenseLayer;
+
+typedef struct GRULayer {
+    const float *bias;
+    const float *input_weights;
+    const float *recurrent_weights;
+    int nb_inputs;
+    int nb_neurons;
+    int activation;
+} GRULayer;
+
+typedef struct RNNModel {
+    int input_dense_size;
+    const DenseLayer *input_dense;
+
+    int vad_gru_size;
+    const GRULayer *vad_gru;
+
+    int noise_gru_size;
+    const GRULayer *noise_gru;
+
+    int denoise_gru_size;
+    const GRULayer *denoise_gru;
+
+    int denoise_output_size;
+    const DenseLayer *denoise_output;
+
+    int vad_output_size;
+    const DenseLayer *vad_output;
+} RNNModel;
+
+typedef struct RNNState {
+    float *vad_gru_state;
+    float *noise_gru_state;
+    float *denoise_gru_state;
+    RNNModel *model;
+} RNNState;
+
+typedef struct DenoiseState {
+    float analysis_mem[FRAME_SIZE];
+    float cepstral_mem[CEPS_MEM][NB_BANDS];
+    int memid;
+    DECLARE_ALIGNED(32, float, synthesis_mem)[FRAME_SIZE];
+    float pitch_buf[PITCH_BUF_SIZE];
+    float pitch_enh_buf[PITCH_BUF_SIZE];
+    float last_gain;
+    int last_period;
+    float mem_hp_x[2];
+    float lastg[NB_BANDS];
+    RNNState rnn;
+    AVTXContext *tx, *txi;
+    av_tx_fn tx_fn, txi_fn;
+} DenoiseState;
+
+typedef struct AudioRNNContext {
+    const AVClass *class;
+
+    char *model_name;
+
+    int channels;
+    DenoiseState *st;
+
+    DECLARE_ALIGNED(32, float, window)[WINDOW_SIZE];
+    float dct_table[NB_BANDS*NB_BANDS];
+
+    RNNModel *model;
+
+    AVFloatDSPContext *fdsp;
+} AudioRNNContext;
+
+#define F_ACTIVATION_TANH       0
+#define F_ACTIVATION_SIGMOID    1
+#define F_ACTIVATION_RELU       2
+
+static void rnnoise_model_free(RNNModel *model)
+{
+#define FREE_MAYBE(ptr) do { if (ptr) free(ptr); } while (0)
+#define FREE_DENSE(name) do { \
+    if (model->name) { \
+        av_free((void *) model->name->input_weights); \
+        av_free((void *) model->name->bias); \
+        av_free((void *) model->name); \
+    } \
+    } while (0)
+#define FREE_GRU(name) do { \
+    if (model->name) { \
+        av_free((void *) model->name->input_weights); \
+        av_free((void *) model->name->recurrent_weights); \
+        av_free((void *) model->name->bias); \
+        av_free((void *) model->name); \
+    } \
+    } while (0)
+
+    if (!model)
+        return;
+    FREE_DENSE(input_dense);
+    FREE_GRU(vad_gru);
+    FREE_GRU(noise_gru);
+    FREE_GRU(denoise_gru);
+    FREE_DENSE(denoise_output);
+    FREE_DENSE(vad_output);
+    av_free(model);
+}
+
+static RNNModel *rnnoise_model_from_file(FILE *f)
+{
+    RNNModel *ret;
+    DenseLayer *input_dense;
+    GRULayer *vad_gru;
+    GRULayer *noise_gru;
+    GRULayer *denoise_gru;
+    DenseLayer *denoise_output;
+    DenseLayer *vad_output;
+    int in;
+
+    if (fscanf(f, "rnnoise-nu model file version %d\n", &in) != 1 || in != 1)
+        return NULL;
+
+    ret = av_calloc(1, sizeof(RNNModel));
+    if (!ret)
+        return NULL;
+
+#define ALLOC_LAYER(type, name) \
+    name = av_calloc(1, sizeof(type)); \
+    if (!name) { \
+        rnnoise_model_free(ret); \
+        return NULL; \
+    } \
+    ret->name = name
+
+    ALLOC_LAYER(DenseLayer, input_dense);
+    ALLOC_LAYER(GRULayer, vad_gru);
+    ALLOC_LAYER(GRULayer, noise_gru);
+    ALLOC_LAYER(GRULayer, denoise_gru);
+    ALLOC_LAYER(DenseLayer, denoise_output);
+    ALLOC_LAYER(DenseLayer, vad_output);
+
+#define INPUT_VAL(name) do { \
+    if (fscanf(f, "%d", &in) != 1 || in < 0 || in > 128) { \
+        rnnoise_model_free(ret); \
+        return NULL; \
+    } \
+    name = in; \
+    } while (0)
+
+#define INPUT_ACTIVATION(name) do { \
+    int activation; \
+    INPUT_VAL(activation); \
+    switch (activation) { \
+    case F_ACTIVATION_SIGMOID: \
+        name = ACTIVATION_SIGMOID; \
+        break; \
+    case F_ACTIVATION_RELU: \
+        name = ACTIVATION_RELU; \
+        break; \
+    default: \
+        name = ACTIVATION_TANH; \
+    } \
+    } while (0)
+
+#define INPUT_ARRAY(name, len) do { \
+    float *values = av_calloc((len), sizeof(float)); \
+    if (!values) { \
+        rnnoise_model_free(ret); \
+        return NULL; \
+    } \
+    name = values; \
+    for (int i = 0; i < (len); i++) { \
+        if (fscanf(f, "%d", &in) != 1) { \
+            rnnoise_model_free(ret); \
+            return NULL; \
+        } \
+        values[i] = in; \
+    } \
+    } while (0)
+
+#define INPUT_ARRAY3(name, len0, len1, len2) do { \
+    float *values = av_calloc(FFALIGN((len0), 4) * FFALIGN((len1), 4) * (len2), sizeof(float)); \
+    if (!values) { \
+        rnnoise_model_free(ret); \
+        return NULL; \
+    } \
+    name = values; \
+    for (int k = 0; k < (len0); k++) { \
+        for (int i = 0; i < (len2); i++) { \
+            for (int j = 0; j < (len1); j++) { \
+                if (fscanf(f, "%d", &in) != 1) { \
+                    rnnoise_model_free(ret); \
+                    return NULL; \
+                } \
+                values[j * (len2) * FFALIGN((len0), 4) + i * FFALIGN((len0), 4) + k] = in; \
+            } \
+        } \
+    } \
+    } while (0)
+
+#define INPUT_DENSE(name) do { \
+    INPUT_VAL(name->nb_inputs); \
+    INPUT_VAL(name->nb_neurons); \
+    ret->name ## _size = name->nb_neurons; \
+    INPUT_ACTIVATION(name->activation); \
+    INPUT_ARRAY(name->input_weights, name->nb_inputs * name->nb_neurons); \
+    INPUT_ARRAY(name->bias, name->nb_neurons); \
+    } while (0)
+
+#define INPUT_GRU(name) do { \
+    INPUT_VAL(name->nb_inputs); \
+    INPUT_VAL(name->nb_neurons); \
+    ret->name ## _size = name->nb_neurons; \
+    INPUT_ACTIVATION(name->activation); \
+    INPUT_ARRAY3(name->input_weights, name->nb_inputs, name->nb_neurons, 3); \
+    INPUT_ARRAY3(name->recurrent_weights, name->nb_neurons, name->nb_neurons, 3); \
+    INPUT_ARRAY(name->bias, name->nb_neurons * 3); \
+    } while (0)
+
+    INPUT_DENSE(input_dense);
+    INPUT_GRU(vad_gru);
+    INPUT_GRU(noise_gru);
+    INPUT_GRU(denoise_gru);
+    INPUT_DENSE(denoise_output);
+    INPUT_DENSE(vad_output);
+
+    return ret;
+}
+
+static int query_formats(AVFilterContext *ctx)
+{
+    AVFilterFormats *formats = NULL;
+    AVFilterChannelLayouts *layouts = NULL;
+    static const enum AVSampleFormat sample_fmts[] = {
+        AV_SAMPLE_FMT_FLTP,
+        AV_SAMPLE_FMT_NONE
+    };
+    int ret, sample_rates[] = { 48000, -1 };
+
+    formats = ff_make_format_list(sample_fmts);
+    if (!formats)
+        return AVERROR(ENOMEM);
+    ret = ff_set_common_formats(ctx, formats);
+    if (ret < 0)
+        return ret;
+
+    layouts = ff_all_channel_counts();
+    if (!layouts)
+        return AVERROR(ENOMEM);
+
+    ret = ff_set_common_channel_layouts(ctx, layouts);
+    if (ret < 0)
+        return ret;
+
+    formats = ff_make_format_list(sample_rates);
+    if (!formats)
+        return AVERROR(ENOMEM);
+    return ff_set_common_samplerates(ctx, formats);
+}
+
+static int config_input(AVFilterLink *inlink)
+{
+    AVFilterContext *ctx = inlink->dst;
+    AudioRNNContext *s = ctx->priv;
+    int ret;
+
+    s->channels = inlink->channels;
+
+    s->st = av_calloc(s->channels, sizeof(DenoiseState));
+    if (!s->st)
+        return AVERROR(ENOMEM);
+
+    for (int i = 0; i < s->channels; i++) {
+        DenoiseState *st = &s->st[i];
+
+        st->rnn.model = s->model;
+        st->rnn.vad_gru_state = av_calloc(sizeof(float), FFALIGN(s->model->vad_gru_size, 16));
+        st->rnn.noise_gru_state = av_calloc(sizeof(float), FFALIGN(s->model->noise_gru_size, 16));
+        st->rnn.denoise_gru_state = av_calloc(sizeof(float), FFALIGN(s->model->denoise_gru_size, 16));
+        if (!st->rnn.vad_gru_state ||
+            !st->rnn.noise_gru_state ||
+            !st->rnn.denoise_gru_state)
+            return AVERROR(ENOMEM);
+
+        ret = av_tx_init(&st->tx, &st->tx_fn, AV_TX_FLOAT_FFT, 0, WINDOW_SIZE, NULL, 0);
+        if (ret < 0)
+            return ret;
+
+        ret = av_tx_init(&st->txi, &st->txi_fn, AV_TX_FLOAT_FFT, 1, WINDOW_SIZE, NULL, 0);
+        if (ret < 0)
+            return ret;
+    }
+
+    return 0;
+}
+
+static void biquad(float *y, float mem[2], const float *x,
+                   const float *b, const float *a, int N)
+{
+    for (int i = 0; i < N; i++) {
+        float xi, yi;
+
+        xi = x[i];
+        yi = x[i] + mem[0];
+        mem[0] = mem[1] + (b[0]*xi - a[0]*yi);
+        mem[1] = (b[1]*xi - a[1]*yi);
+        y[i] = yi;
+    }
+}
+
+#define RNN_MOVE(dst, src, n) (memmove((dst), (src), (n)*sizeof(*(dst)) + 0*((dst)-(src)) ))
+#define RNN_CLEAR(dst, n) (memset((dst), 0, (n)*sizeof(*(dst))))
+#define RNN_COPY(dst, src, n) (memcpy((dst), (src), (n)*sizeof(*(dst)) + 0*((dst)-(src)) ))
+
+static void forward_transform(DenoiseState *st, AVComplexFloat *out, const float *in)
+{
+    AVComplexFloat x[WINDOW_SIZE];
+    AVComplexFloat y[WINDOW_SIZE];
+
+    for (int i = 0; i < WINDOW_SIZE; i++) {
+        x[i].re = in[i];
+        x[i].im = 0;
+    }
+
+    st->tx_fn(st->tx, y, x, sizeof(float));
+
+    RNN_COPY(out, y, FREQ_SIZE);
+}
+
+static void inverse_transform(DenoiseState *st, float *out, const AVComplexFloat *in)
+{
+    AVComplexFloat x[WINDOW_SIZE];
+    AVComplexFloat y[WINDOW_SIZE];
+
+    for (int i = 0; i < FREQ_SIZE; i++)
+        x[i] = in[i];
+
+    for (int i = FREQ_SIZE; i < WINDOW_SIZE; i++) {
+        x[i].re =  x[WINDOW_SIZE - i].re;
+        x[i].im = -x[WINDOW_SIZE - i].im;
+    }
+
+    st->txi_fn(st->txi, y, x, sizeof(float));
+
+    for (int i = 0; i < WINDOW_SIZE; i++)
+        out[i] = y[i].re / WINDOW_SIZE;
+}
+
+static const uint8_t eband5ms[] = {
+/*0  200 400 600 800  1k 1.2 1.4 1.6  2k 2.4 2.8 3.2  4k 4.8 5.6 6.8  8k 9.6 12k 15.6 20k*/
+  0,  1,  2,  3,  4,   5, 6,  7,  8,  10, 12, 14, 16, 20, 24, 28, 34, 40, 48, 60, 78, 100
+};
+
+static void compute_band_energy(float *bandE, const AVComplexFloat *X)
+{
+    float sum[NB_BANDS] = {0};
+
+    for (int i = 0; i < NB_BANDS - 1; i++) {
+        int band_size;
+
+        band_size = (eband5ms[i + 1] - eband5ms[i]) << FRAME_SIZE_SHIFT;
+        for (int j = 0; j < band_size; j++) {
+            float tmp, frac = (float)j / band_size;
+
+            tmp         = SQUARE(X[(eband5ms[i] << FRAME_SIZE_SHIFT) + j].re);
+            tmp        += SQUARE(X[(eband5ms[i] << FRAME_SIZE_SHIFT) + j].im);
+            sum[i]     += (1.f - frac) * tmp;
+            sum[i + 1] +=        frac  * tmp;
+        }
+    }
+
+    sum[0] *= 2;
+    sum[NB_BANDS - 1] *= 2;
+
+    for (int i = 0; i < NB_BANDS; i++)
+        bandE[i] = sum[i];
+}
+
+static void compute_band_corr(float *bandE, const AVComplexFloat *X, const AVComplexFloat *P)
+{
+    float sum[NB_BANDS] = { 0 };
+
+    for (int i = 0; i < NB_BANDS - 1; i++) {
+        int band_size;
+
+        band_size = (eband5ms[i + 1] - eband5ms[i]) << FRAME_SIZE_SHIFT;
+        for (int j = 0; j < band_size; j++) {
+            float tmp, frac = (float)j / band_size;
+
+            tmp  = X[(eband5ms[i]<<FRAME_SIZE_SHIFT) + j].re * P[(eband5ms[i]<<FRAME_SIZE_SHIFT) + j].re;
+            tmp += X[(eband5ms[i]<<FRAME_SIZE_SHIFT) + j].im * P[(eband5ms[i]<<FRAME_SIZE_SHIFT) + j].im;
+            sum[i]     += (1 - frac) * tmp;
+            sum[i + 1] +=      frac  * tmp;
+        }
+    }
+
+    sum[0] *= 2;
+    sum[NB_BANDS-1] *= 2;
+
+    for (int i = 0; i < NB_BANDS; i++)
+        bandE[i] = sum[i];
+}
+
+static void frame_analysis(AudioRNNContext *s, DenoiseState *st, AVComplexFloat *X, float *Ex, const float *in)
+{
+    LOCAL_ALIGNED_32(float, x, [WINDOW_SIZE]);
+
+    RNN_COPY(x, st->analysis_mem, FRAME_SIZE);
+    RNN_COPY(x + FRAME_SIZE, in, FRAME_SIZE);
+    RNN_COPY(st->analysis_mem, in, FRAME_SIZE);
+    s->fdsp->vector_fmul(x, x, s->window, WINDOW_SIZE);
+    forward_transform(st, X, x);
+    compute_band_energy(Ex, X);
+}
+
+static void frame_synthesis(AudioRNNContext *s, DenoiseState *st, float *out, const AVComplexFloat *y)
+{
+    LOCAL_ALIGNED_32(float, x, [WINDOW_SIZE]);
+
+    inverse_transform(st, x, y);
+    s->fdsp->vector_fmul(x, x, s->window, WINDOW_SIZE);
+    s->fdsp->vector_fmac_scalar(x, st->synthesis_mem, 1.f, FRAME_SIZE);
+    RNN_COPY(out, x, FRAME_SIZE);
+    RNN_COPY(st->synthesis_mem, &x[FRAME_SIZE], FRAME_SIZE);
+}
+
+static inline void xcorr_kernel(const float *x, const float *y, float sum[4], int len)
+{
+    float y_0, y_1, y_2, y_3 = 0;
+    int j;
+
+    y_0 = *y++;
+    y_1 = *y++;
+    y_2 = *y++;
+
+    for (j = 0; j < len - 3; j += 4) {
+        float tmp;
+
+        tmp = *x++;
+        y_3 = *y++;
+        sum[0] += tmp * y_0;
+        sum[1] += tmp * y_1;
+        sum[2] += tmp * y_2;
+        sum[3] += tmp * y_3;
+        tmp = *x++;
+        y_0 = *y++;
+        sum[0] += tmp * y_1;
+        sum[1] += tmp * y_2;
+        sum[2] += tmp * y_3;
+        sum[3] += tmp * y_0;
+        tmp = *x++;
+        y_1 = *y++;
+        sum[0] += tmp * y_2;
+        sum[1] += tmp * y_3;
+        sum[2] += tmp * y_0;
+        sum[3] += tmp * y_1;
+        tmp = *x++;
+        y_2 = *y++;
+        sum[0] += tmp * y_3;
+        sum[1] += tmp * y_0;
+        sum[2] += tmp * y_1;
+        sum[3] += tmp * y_2;
+    }
+
+    if (j++ < len) {
+        float tmp = *x++;
+
+        y_3 = *y++;
+        sum[0] += tmp * y_0;
+        sum[1] += tmp * y_1;
+        sum[2] += tmp * y_2;
+        sum[3] += tmp * y_3;
+    }
+
+    if (j++ < len) {
+        float tmp=*x++;
+
+        y_0 = *y++;
+        sum[0] += tmp * y_1;
+        sum[1] += tmp * y_2;
+        sum[2] += tmp * y_3;
+        sum[3] += tmp * y_0;
+    }
+
+    if (j < len) {
+        float tmp=*x++;
+
+        y_1 = *y++;
+        sum[0] += tmp * y_2;
+        sum[1] += tmp * y_3;
+        sum[2] += tmp * y_0;
+        sum[3] += tmp * y_1;
+    }
+}
+
+static inline float celt_inner_prod(const float *x,
+                                    const float *y, int N)
+{
+    float xy = 0.f;
+
+    for (int i = 0; i < N; i++)
+        xy += x[i] * y[i];
+
+    return xy;
+}
+
+static void celt_pitch_xcorr(const float *x, const float *y,
+                             float *xcorr, int len, int max_pitch)
+{
+    int i;
+
+    for (i = 0; i < max_pitch - 3; i += 4) {
+        float sum[4] = { 0, 0, 0, 0};
+
+        xcorr_kernel(x, y + i, sum, len);
+
+        xcorr[i]     = sum[0];
+        xcorr[i + 1] = sum[1];
+        xcorr[i + 2] = sum[2];
+        xcorr[i + 3] = sum[3];
+    }
+    /* In case max_pitch isn't a multiple of 4, do non-unrolled version. */
+    for (; i < max_pitch; i++) {
+        xcorr[i] = celt_inner_prod(x, y + i, len);
+    }
+}
+
+static int celt_autocorr(const float *x,   /*  in: [0...n-1] samples x   */
+                         float       *ac,  /* out: [0...lag-1] ac values */
+                         const float *window,
+                         int          overlap,
+                         int          lag,
+                         int          n)
+{
+    int fastN = n - lag;
+    int shift;
+    const float *xptr;
+    float xx[PITCH_BUF_SIZE>>1];
+
+    if (overlap == 0) {
+        xptr = x;
+    } else {
+        for (int i = 0; i < n; i++)
+            xx[i] = x[i];
+        for (int i = 0; i < overlap; i++) {
+            xx[i] = x[i] * window[i];
+            xx[n-i-1] = x[n-i-1] * window[i];
+        }
+        xptr = xx;
+    }
+
+    shift = 0;
+    celt_pitch_xcorr(xptr, xptr, ac, fastN, lag+1);
+
+    for (int k = 0; k <= lag; k++) {
+        float d = 0.f;
+
+        for (int i = k + fastN; i < n; i++)
+            d += xptr[i] * xptr[i-k];
+        ac[k] += d;
+    }
+
+    return shift;
+}
+
+static void celt_lpc(float *lpc, /* out: [0...p-1] LPC coefficients      */
+                const float *ac,   /* in:  [0...p] autocorrelation values  */
+                          int p)
+{
+    float r, error = ac[0];
+
+    RNN_CLEAR(lpc, p);
+    if (ac[0] != 0) {
+        for (int i = 0; i < p; i++) {
+            /* Sum up this iteration's reflection coefficient */
+            float rr = 0;
+            for (int j = 0; j < i; j++)
+                rr += (lpc[j] * ac[i - j]);
+            rr += ac[i + 1];
+            r = -rr/error;
+            /*  Update LPC coefficients and total error */
+            lpc[i] = r;
+            for (int j = 0; j < (i + 1) >> 1; j++) {
+                float tmp1, tmp2;
+                tmp1 = lpc[j];
+                tmp2 = lpc[i-1-j];
+                lpc[j]     = tmp1 + (r*tmp2);
+                lpc[i-1-j] = tmp2 + (r*tmp1);
+            }
+
+            error = error - (r * r *error);
+            /* Bail out once we get 30 dB gain */
+            if (error < .001f * ac[0])
+                break;
+        }
+    }
+}
+
+static void celt_fir5(const float *x,
+                      const float *num,
+                      float *y,
+                      int N,
+                      float *mem)
+{
+    float num0, num1, num2, num3, num4;
+    float mem0, mem1, mem2, mem3, mem4;
+
+    num0 = num[0];
+    num1 = num[1];
+    num2 = num[2];
+    num3 = num[3];
+    num4 = num[4];
+    mem0 = mem[0];
+    mem1 = mem[1];
+    mem2 = mem[2];
+    mem3 = mem[3];
+    mem4 = mem[4];
+
+    for (int i = 0; i < N; i++) {
+        float sum = x[i];
+
+        sum += (num0*mem0);
+        sum += (num1*mem1);
+        sum += (num2*mem2);
+        sum += (num3*mem3);
+        sum += (num4*mem4);
+        mem4 = mem3;
+        mem3 = mem2;
+        mem2 = mem1;
+        mem1 = mem0;
+        mem0 = x[i];
+        y[i] = sum;
+    }
+
+    mem[0] = mem0;
+    mem[1] = mem1;
+    mem[2] = mem2;
+    mem[3] = mem3;
+    mem[4] = mem4;
+}
+
+static void pitch_downsample(float *x[], float *x_lp,
+                             int len, int C)
+{
+    float ac[5];
+    float tmp=Q15ONE;
+    float lpc[4], mem[5]={0,0,0,0,0};
+    float lpc2[5];
+    float c1 = .8f;
+
+    for (int i = 1; i < len >> 1; i++)
+        x_lp[i] = .5f * (.5f * (x[0][(2*i-1)]+x[0][(2*i+1)])+x[0][2*i]);
+    x_lp[0] = .5f * (.5f * (x[0][1])+x[0][0]);
+    if (C==2) {
+        for (int i = 1; i < len >> 1; i++)
+            x_lp[i] += (.5f * (.5f * (x[1][(2*i-1)]+x[1][(2*i+1)])+x[1][2*i]));
+        x_lp[0] += .5f * (.5f * (x[1][1])+x[1][0]);
+    }
+
+    celt_autocorr(x_lp, ac, NULL, 0, 4, len>>1);
+
+    /* Noise floor -40 dB */
+    ac[0] *= 1.0001f;
+    /* Lag windowing */
+    for (int i = 1; i <= 4; i++) {
+        /*ac[i] *= exp(-.5*(2*M_PI*.002*i)*(2*M_PI*.002*i));*/
+        ac[i] -= ac[i]*(.008f*i)*(.008f*i);
+    }
+
+    celt_lpc(lpc, ac, 4);
+    for (int i = 0; i < 4; i++) {
+        tmp = .9f * tmp;
+        lpc[i] = (lpc[i] * tmp);
+    }
+    /* Add a zero */
+    lpc2[0] = lpc[0] + .8f;
+    lpc2[1] = lpc[1] + (c1 * lpc[0]);
+    lpc2[2] = lpc[2] + (c1 * lpc[1]);
+    lpc2[3] = lpc[3] + (c1 * lpc[2]);
+    lpc2[4] = (c1 * lpc[3]);
+    celt_fir5(x_lp, lpc2, x_lp, len>>1, mem);
+}
+
+static inline void dual_inner_prod(const float *x, const float *y01, const float *y02,
+                                   int N, float *xy1, float *xy2)
+{
+    float xy01 = 0, xy02 = 0;
+
+    for (int i = 0; i < N; i++) {
+        xy01 += (x[i] * y01[i]);
+        xy02 += (x[i] * y02[i]);
+    }
+
+    *xy1 = xy01;
+    *xy2 = xy02;
+}
+
+static float compute_pitch_gain(float xy, float xx, float yy)
+{
+    return xy / sqrtf(1.f + xx * yy);
+}
+
+static const int second_check[16] = {0, 0, 3, 2, 3, 2, 5, 2, 3, 2, 3, 2, 5, 2, 3, 2};
+static const float remove_doubling(float *x, int maxperiod, int minperiod,
+                                   int N, int *T0_, int prev_period, float prev_gain)
+{
+    int k, i, T, T0;
+    float g, g0;
+    float pg;
+    float xy,xx,yy,xy2;
+    float xcorr[3];
+    float best_xy, best_yy;
+    int offset;
+    int minperiod0;
+    float yy_lookup[PITCH_MAX_PERIOD+1];
+
+    minperiod0 = minperiod;
+    maxperiod /= 2;
+    minperiod /= 2;
+    *T0_ /= 2;
+    prev_period /= 2;
+    N /= 2;
+    x += maxperiod;
+    if (*T0_>=maxperiod)
+        *T0_=maxperiod-1;
+
+    T = T0 = *T0_;
+    dual_inner_prod(x, x, x-T0, N, &xx, &xy);
+    yy_lookup[0] = xx;
+    yy=xx;
+    for (i = 1; i <= maxperiod; i++) {
+        yy = yy+(x[-i] * x[-i])-(x[N-i] * x[N-i]);
+        yy_lookup[i] = FFMAX(0, yy);
+    }
+    yy = yy_lookup[T0];
+    best_xy = xy;
+    best_yy = yy;
+    g = g0 = compute_pitch_gain(xy, xx, yy);
+    /* Look for any pitch at T/k */
+    for (k = 2; k <= 15; k++) {
+        int T1, T1b;
+        float g1;
+        float cont=0;
+        float thresh;
+        T1 = (2*T0+k)/(2*k);
+        if (T1 < minperiod)
+            break;
+        /* Look for another strong correlation at T1b */
+        if (k==2)
+        {
+            if (T1+T0>maxperiod)
+                T1b = T0;
+            else
+                T1b = T0+T1;
+        } else
+        {
+            T1b = (2*second_check[k]*T0+k)/(2*k);
+        }
+        dual_inner_prod(x, &x[-T1], &x[-T1b], N, &xy, &xy2);
+        xy = .5f * (xy + xy2);
+        yy = .5f * (yy_lookup[T1] + yy_lookup[T1b]);
+        g1 = compute_pitch_gain(xy, xx, yy);
+        if (FFABS(T1-prev_period)<=1)
+            cont = prev_gain;
+        else if (FFABS(T1-prev_period)<=2 && 5 * k * k < T0)
+            cont = prev_gain * .5f;
+        else
+            cont = 0;
+        thresh = FFMAX(.3f, (.7f * g0) - cont);
+        /* Bias against very high pitch (very short period) to avoid false-positives
+           due to short-term correlation */
+        if (T1<3*minperiod)
+            thresh = FFMAX(.4f, (.85f * g0) - cont);
+        else if (T1<2*minperiod)
+            thresh = FFMAX(.5f, (.9f * g0) - cont);
+        if (g1 > thresh)
+        {
+            best_xy = xy;
+            best_yy = yy;
+            T = T1;
+            g = g1;
+        }
+    }
+    best_xy = FFMAX(0, best_xy);
+    if (best_yy <= best_xy)
+        pg = Q15ONE;
+    else
+        pg = best_xy/(best_yy + 1);
+
+    for (k = 0; k < 3; k++)
+        xcorr[k] = celt_inner_prod(x, x-(T+k-1), N);
+    if ((xcorr[2]-xcorr[0]) > .7f * (xcorr[1]-xcorr[0]))
+        offset = 1;
+    else if ((xcorr[0]-xcorr[2]) > (.7f * (xcorr[1] - xcorr[2])))
+        offset = -1;
+    else
+        offset = 0;
+    if (pg > g)
+        pg = g;
+    *T0_ = 2*T+offset;
+
+    if (*T0_<minperiod0)
+        *T0_=minperiod0;
+    return pg;
+}
+
+static void find_best_pitch(float *xcorr, float *y, int len,
+                            int max_pitch, int *best_pitch)
+{
+    float best_num[2];
+    float best_den[2];
+    float Syy = 1.f;
+
+    best_num[0] = -1;
+    best_num[1] = -1;
+    best_den[0] = 0;
+    best_den[1] = 0;
+    best_pitch[0] = 0;
+    best_pitch[1] = 1;
+
+    for (int j = 0; j < len; j++)
+        Syy += y[j] * y[j];
+
+    for (int i = 0; i < max_pitch; i++) {
+        if (xcorr[i]>0) {
+            float num;
+            float xcorr16;
+
+            xcorr16 = xcorr[i];
+            /* Considering the range of xcorr16, this should avoid both underflows
+               and overflows (inf) when squaring xcorr16 */
+            xcorr16 *= 1e-12f;
+            num = xcorr16 * xcorr16;
+            if ((num * best_den[1]) > (best_num[1] * Syy)) {
+                if ((num * best_den[0]) > (best_num[0] * Syy)) {
+                    best_num[1] = best_num[0];
+                    best_den[1] = best_den[0];
+                    best_pitch[1] = best_pitch[0];
+                    best_num[0] = num;
+                    best_den[0] = Syy;
+                    best_pitch[0] = i;
+                } else {
+                    best_num[1] = num;
+                    best_den[1] = Syy;
+                    best_pitch[1] = i;
+                }
+            }
+        }
+        Syy += y[i+len]*y[i+len] - y[i] * y[i];
+        Syy = FFMAX(1, Syy);
+    }
+}
+
+static void pitch_search(const float *x_lp, float *y,
+                         int len, int max_pitch, int *pitch)
+{
+    int lag;
+    int best_pitch[2]={0,0};
+    int offset;
+
+    float x_lp4[WINDOW_SIZE];
+    float y_lp4[WINDOW_SIZE];
+    float xcorr[WINDOW_SIZE];
+
+    lag = len+max_pitch;
+
+    /* Downsample by 2 again */
+    for (int j = 0; j < len >> 2; j++)
+        x_lp4[j] = x_lp[2*j];
+    for (int j = 0; j < lag >> 2; j++)
+        y_lp4[j] = y[2*j];
+
+    /* Coarse search with 4x decimation */
+
+    celt_pitch_xcorr(x_lp4, y_lp4, xcorr, len>>2, max_pitch>>2);
+
+    find_best_pitch(xcorr, y_lp4, len>>2, max_pitch>>2, best_pitch);
+
+    /* Finer search with 2x decimation */
+    for (int i = 0; i < max_pitch >> 1; i++) {
+        float sum;
+        xcorr[i] = 0;
+        if (FFABS(i-2*best_pitch[0])>2 && FFABS(i-2*best_pitch[1])>2)
+            continue;
+        sum = celt_inner_prod(x_lp, y+i, len>>1);
+        xcorr[i] = FFMAX(-1, sum);
+    }
+
+    find_best_pitch(xcorr, y, len>>1, max_pitch>>1, best_pitch);
+
+    /* Refine by pseudo-interpolation */
+    if (best_pitch[0] > 0 && best_pitch[0] < (max_pitch >> 1) - 1) {
+        float a, b, c;
+
+        a = xcorr[best_pitch[0] - 1];
+        b = xcorr[best_pitch[0]];
+        c = xcorr[best_pitch[0] + 1];
+        if (c - a > .7f * (b - a))
+            offset = 1;
+        else if (a - c > .7f * (b-c))
+            offset = -1;
+        else
+            offset = 0;
+    } else {
+        offset = 0;
+    }
+
+    *pitch = 2 * best_pitch[0] - offset;
+}
+
+static void dct(AudioRNNContext *s, float *out, const float *in)
+{
+    for (int i = 0; i < NB_BANDS; i++) {
+        float sum = 0.f;
+
+        for (int j = 0; j < NB_BANDS; j++) {
+            sum += in[j] * s->dct_table[j * NB_BANDS + i];
+        }
+        out[i] = sum * sqrtf(2.f / 22);
+    }
+}
+
+static int compute_frame_features(AudioRNNContext *s, DenoiseState *st, AVComplexFloat *X, AVComplexFloat *P,
+                                  float *Ex, float *Ep, float *Exp, float *features, const float *in)
+{
+    float E = 0;
+    float *ceps_0, *ceps_1, *ceps_2;
+    float spec_variability = 0;
+    float Ly[NB_BANDS];
+    LOCAL_ALIGNED_32(float, p, [WINDOW_SIZE]);
+    float pitch_buf[PITCH_BUF_SIZE>>1];
+    int pitch_index;
+    float gain;
+    float *(pre[1]);
+    float tmp[NB_BANDS];
+    float follow, logMax;
+
+    frame_analysis(s, st, X, Ex, in);
+    RNN_MOVE(st->pitch_buf, &st->pitch_buf[FRAME_SIZE], PITCH_BUF_SIZE-FRAME_SIZE);
+    RNN_COPY(&st->pitch_buf[PITCH_BUF_SIZE-FRAME_SIZE], in, FRAME_SIZE);
+    pre[0] = &st->pitch_buf[0];
+    pitch_downsample(pre, pitch_buf, PITCH_BUF_SIZE, 1);
+    pitch_search(pitch_buf+(PITCH_MAX_PERIOD>>1), pitch_buf, PITCH_FRAME_SIZE,
+            PITCH_MAX_PERIOD-3*PITCH_MIN_PERIOD, &pitch_index);
+    pitch_index = PITCH_MAX_PERIOD-pitch_index;
+
+    gain = remove_doubling(pitch_buf, PITCH_MAX_PERIOD, PITCH_MIN_PERIOD,
+            PITCH_FRAME_SIZE, &pitch_index, st->last_period, st->last_gain);
+    st->last_period = pitch_index;
+    st->last_gain = gain;
+
+    for (int i = 0; i < WINDOW_SIZE; i++)
+        p[i] = st->pitch_buf[PITCH_BUF_SIZE-WINDOW_SIZE-pitch_index+i];
+
+    s->fdsp->vector_fmul(p, p, s->window, WINDOW_SIZE);
+    forward_transform(st, P, p);
+    compute_band_energy(Ep, P);
+    compute_band_corr(Exp, X, P);
+
+    for (int i = 0; i < NB_BANDS; i++)
+        Exp[i] = Exp[i] / sqrtf(.001f+Ex[i]*Ep[i]);
+
+    dct(s, tmp, Exp);
+
+    for (int i = 0; i < NB_DELTA_CEPS; i++)
+        features[NB_BANDS+2*NB_DELTA_CEPS+i] = tmp[i];
+
+    features[NB_BANDS+2*NB_DELTA_CEPS] -= 1.3;
+    features[NB_BANDS+2*NB_DELTA_CEPS+1] -= 0.9;
+    features[NB_BANDS+3*NB_DELTA_CEPS] = .01*(pitch_index-300);
+    logMax = -2;
+    follow = -2;
+
+    for (int i = 0; i < NB_BANDS; i++) {
+        Ly[i] = log10f(1e-2f + Ex[i]);
+        Ly[i] = FFMAX(logMax-7, FFMAX(follow-1.5, Ly[i]));
+        logMax = FFMAX(logMax, Ly[i]);
+        follow = FFMAX(follow-1.5, Ly[i]);
+        E += Ex[i];
+    }
+
+    if (E < 0.04f) {
+        /* If there's no audio, avoid messing up the state. */
+        RNN_CLEAR(features, NB_FEATURES);
+        return 1;
+    }
+
+    dct(s, features, Ly);
+    features[0] -= 12;
+    features[1] -= 4;
+    ceps_0 = st->cepstral_mem[st->memid];
+    ceps_1 = (st->memid < 1) ? st->cepstral_mem[CEPS_MEM+st->memid-1] : st->cepstral_mem[st->memid-1];
+    ceps_2 = (st->memid < 2) ? st->cepstral_mem[CEPS_MEM+st->memid-2] : st->cepstral_mem[st->memid-2];
+
+    for (int i = 0; i < NB_BANDS; i++)
+        ceps_0[i] = features[i];
+
+    st->memid++;
+    for (int i = 0; i < NB_DELTA_CEPS; i++) {
+        features[i] = ceps_0[i] + ceps_1[i] + ceps_2[i];
+        features[NB_BANDS+i] = ceps_0[i] - ceps_2[i];
+        features[NB_BANDS+NB_DELTA_CEPS+i] =  ceps_0[i] - 2*ceps_1[i] + ceps_2[i];
+    }
+    /* Spectral variability features. */
+    if (st->memid == CEPS_MEM)
+        st->memid = 0;
+
+    for (int i = 0; i < CEPS_MEM; i++) {
+        float mindist = 1e15f;
+        for (int j = 0; j < CEPS_MEM; j++) {
+            float dist = 0.f;
+            for (int k = 0; k < NB_BANDS; k++) {
+                float tmp;
+
+                tmp = st->cepstral_mem[i][k] - st->cepstral_mem[j][k];
+                dist += tmp*tmp;
+            }
+
+            if (j != i)
+                mindist = FFMIN(mindist, dist);
+        }
+
+        spec_variability += mindist;
+    }
+
+    features[NB_BANDS+3*NB_DELTA_CEPS+1] = spec_variability/CEPS_MEM-2.1;
+
+    return 0;
+}
+
+static void interp_band_gain(float *g, const float *bandE)
+{
+    memset(g, 0, sizeof(*g) * FREQ_SIZE);
+
+    for (int i = 0; i < NB_BANDS - 1; i++) {
+        const int band_size = (eband5ms[i + 1] - eband5ms[i]) << FRAME_SIZE_SHIFT;
+
+        for (int j = 0; j < band_size; j++) {
+            float frac = (float)j / band_size;
+
+            g[(eband5ms[i] << FRAME_SIZE_SHIFT) + j] = (1.f - frac) * bandE[i] + frac * bandE[i + 1];
+        }
+    }
+}
+
+static void pitch_filter(AVComplexFloat *X, const AVComplexFloat *P, const float *Ex, const float *Ep,
+                         const float *Exp, const float *g)
+{
+    float newE[NB_BANDS];
+    float r[NB_BANDS];
+    float norm[NB_BANDS];
+    float rf[FREQ_SIZE] = {0};
+    float normf[FREQ_SIZE]={0};
+
+    for (int i = 0; i < NB_BANDS; i++) {
+        if (Exp[i]>g[i]) r[i] = 1;
+        else r[i] = SQUARE(Exp[i])*(1-SQUARE(g[i]))/(.001 + SQUARE(g[i])*(1-SQUARE(Exp[i])));
+        r[i]  = sqrtf(av_clipf(r[i], 0, 1));
+        r[i] *= sqrtf(Ex[i]/(1e-8+Ep[i]));
+    }
+    interp_band_gain(rf, r);
+    for (int i = 0; i < FREQ_SIZE; i++) {
+        X[i].re += rf[i]*P[i].re;
+        X[i].im += rf[i]*P[i].im;
+    }
+    compute_band_energy(newE, X);
+    for (int i = 0; i < NB_BANDS; i++) {
+        norm[i] = sqrtf(Ex[i] / (1e-8+newE[i]));
+    }
+    interp_band_gain(normf, norm);
+    for (int i = 0; i < FREQ_SIZE; i++) {
+        X[i].re *= normf[i];
+        X[i].im *= normf[i];
+    }
+}
+
+static const float tansig_table[201] = {
+    0.000000f, 0.039979f, 0.079830f, 0.119427f, 0.158649f,
+    0.197375f, 0.235496f, 0.272905f, 0.309507f, 0.345214f,
+    0.379949f, 0.413644f, 0.446244f, 0.477700f, 0.507977f,
+    0.537050f, 0.564900f, 0.591519f, 0.616909f, 0.641077f,
+    0.664037f, 0.685809f, 0.706419f, 0.725897f, 0.744277f,
+    0.761594f, 0.777888f, 0.793199f, 0.807569f, 0.821040f,
+    0.833655f, 0.845456f, 0.856485f, 0.866784f, 0.876393f,
+    0.885352f, 0.893698f, 0.901468f, 0.908698f, 0.915420f,
+    0.921669f, 0.927473f, 0.932862f, 0.937863f, 0.942503f,
+    0.946806f, 0.950795f, 0.954492f, 0.957917f, 0.961090f,
+    0.964028f, 0.966747f, 0.969265f, 0.971594f, 0.973749f,
+    0.975743f, 0.977587f, 0.979293f, 0.980869f, 0.982327f,
+    0.983675f, 0.984921f, 0.986072f, 0.987136f, 0.988119f,
+    0.989027f, 0.989867f, 0.990642f, 0.991359f, 0.992020f,
+    0.992631f, 0.993196f, 0.993718f, 0.994199f, 0.994644f,
+    0.995055f, 0.995434f, 0.995784f, 0.996108f, 0.996407f,
+    0.996682f, 0.996937f, 0.997172f, 0.997389f, 0.997590f,
+    0.997775f, 0.997946f, 0.998104f, 0.998249f, 0.998384f,
+    0.998508f, 0.998623f, 0.998728f, 0.998826f, 0.998916f,
+    0.999000f, 0.999076f, 0.999147f, 0.999213f, 0.999273f,
+    0.999329f, 0.999381f, 0.999428f, 0.999472f, 0.999513f,
+    0.999550f, 0.999585f, 0.999617f, 0.999646f, 0.999673f,
+    0.999699f, 0.999722f, 0.999743f, 0.999763f, 0.999781f,
+    0.999798f, 0.999813f, 0.999828f, 0.999841f, 0.999853f,
+    0.999865f, 0.999875f, 0.999885f, 0.999893f, 0.999902f,
+    0.999909f, 0.999916f, 0.999923f, 0.999929f, 0.999934f,
+    0.999939f, 0.999944f, 0.999948f, 0.999952f, 0.999956f,
+    0.999959f, 0.999962f, 0.999965f, 0.999968f, 0.999970f,
+    0.999973f, 0.999975f, 0.999977f, 0.999978f, 0.999980f,
+    0.999982f, 0.999983f, 0.999984f, 0.999986f, 0.999987f,
+    0.999988f, 0.999989f, 0.999990f, 0.999990f, 0.999991f,
+    0.999992f, 0.999992f, 0.999993f, 0.999994f, 0.999994f,
+    0.999994f, 0.999995f, 0.999995f, 0.999996f, 0.999996f,
+    0.999996f, 0.999997f, 0.999997f, 0.999997f, 0.999997f,
+    0.999997f, 0.999998f, 0.999998f, 0.999998f, 0.999998f,
+    0.999998f, 0.999998f, 0.999999f, 0.999999f, 0.999999f,
+    0.999999f, 0.999999f, 0.999999f, 0.999999f, 0.999999f,
+    0.999999f, 0.999999f, 0.999999f, 0.999999f, 0.999999f,
+    1.000000f, 1.000000f, 1.000000f, 1.000000f, 1.000000f,
+    1.000000f, 1.000000f, 1.000000f, 1.000000f, 1.000000f,
+    1.000000f,
+};
+
+static inline float tansig_approx(float x)
+{
+    float y, dy;
+    float sign=1;
+    int i;
+
+    /* Tests are reversed to catch NaNs */
+    if (!(x<8))
+        return 1;
+    if (!(x>-8))
+        return -1;
+    /* Another check in case of -ffast-math */
+
+    if (isnan(x))
+       return 0;
+
+    if (x < 0) {
+       x=-x;
+       sign=-1;
+    }
+    i = (int)floor(.5f+25*x);
+    x -= .04f*i;
+    y = tansig_table[i];
+    dy = 1-y*y;
+    y = y + x*dy*(1 - y*x);
+    return sign*y;
+}
+
+static inline float sigmoid_approx(float x)
+{
+    return .5f + .5f*tansig_approx(.5f*x);
+}
+
+static void compute_dense(const DenseLayer *layer, float *output, const float *input)
+{
+    const int N = layer->nb_neurons, M = layer->nb_inputs, stride = N;
+
+    for (int i = 0; i < N; i++) {
+        /* Compute update gate. */
+        float sum = layer->bias[i];
+
+        for (int j = 0; j < M; j++)
+            sum += layer->input_weights[j * stride + i] * input[j];
+
+        output[i] = WEIGHTS_SCALE * sum;
+    }
+
+    if (layer->activation == ACTIVATION_SIGMOID) {
+        for (int i = 0; i < N; i++)
+            output[i] = sigmoid_approx(output[i]);
+    } else if (layer->activation == ACTIVATION_TANH) {
+        for (int i = 0; i < N; i++)
+            output[i] = tansig_approx(output[i]);
+    } else if (layer->activation == ACTIVATION_RELU) {
+        for (int i = 0; i < N; i++)
+            output[i] = FFMAX(0, output[i]);
+    } else {
+        av_assert0(0);
+    }
+}
+
+static void compute_gru(AudioRNNContext *s, const GRULayer *gru, float *state, const float *input)
+{
+    LOCAL_ALIGNED_32(float, z, [MAX_NEURONS]);
+    LOCAL_ALIGNED_32(float, r, [MAX_NEURONS]);
+    LOCAL_ALIGNED_32(float, h, [MAX_NEURONS]);
+    const int M = gru->nb_inputs;
+    const int N = gru->nb_neurons;
+    const int AN = FFALIGN(N, 4);
+    const int AM = FFALIGN(M, 4);
+    const int stride = 3 * AN, istride = 3 * AM;
+
+    for (int i = 0; i < N; i++) {
+        /* Compute update gate. */
+        float sum = gru->bias[i];
+
+        sum += s->fdsp->scalarproduct_float(gru->input_weights + i * istride, input, AM);
+        sum += s->fdsp->scalarproduct_float(gru->recurrent_weights + i * stride, state, AN);
+        z[i] = sigmoid_approx(WEIGHTS_SCALE * sum);
+    }
+
+    for (int i = 0; i < N; i++) {
+        /* Compute reset gate. */
+        float sum = gru->bias[N + i];
+
+        sum += s->fdsp->scalarproduct_float(gru->input_weights + AM + i * istride, input, AM);
+        sum += s->fdsp->scalarproduct_float(gru->recurrent_weights + AN + i * stride, state, AN);
+        r[i] = sigmoid_approx(WEIGHTS_SCALE * sum);
+    }
+
+    for (int i = 0; i < N; i++) {
+        /* Compute output. */
+        float sum = gru->bias[2 * N + i];
+
+        sum += s->fdsp->scalarproduct_float(gru->input_weights + 2 * AM + i * istride, input, AM);
+        for (int j = 0; j < N; j++)
+            sum += gru->recurrent_weights[2 * AN + i * stride + j] * state[j] * r[j];
+
+        if (gru->activation == ACTIVATION_SIGMOID)
+            sum = sigmoid_approx(WEIGHTS_SCALE * sum);
+        else if (gru->activation == ACTIVATION_TANH)
+            sum = tansig_approx(WEIGHTS_SCALE * sum);
+        else if (gru->activation == ACTIVATION_RELU)
+            sum = FFMAX(0, WEIGHTS_SCALE * sum);
+        else
+            av_assert0(0);
+        h[i] = z[i] * state[i] + (1.f - z[i]) * sum;
+    }
+
+    RNN_COPY(state, h, N);
+}
+
+#define INPUT_SIZE 42
+
+static void compute_rnn(AudioRNNContext *s, RNNState *rnn, float *gains, float *vad, const float *input)
+{
+    LOCAL_ALIGNED_32(float, dense_out,     [MAX_NEURONS]);
+    LOCAL_ALIGNED_32(float, noise_input,   [MAX_NEURONS * 3]);
+    LOCAL_ALIGNED_32(float, denoise_input, [MAX_NEURONS * 3]);
+
+    compute_dense(rnn->model->input_dense, dense_out, input);
+    compute_gru(s, rnn->model->vad_gru, rnn->vad_gru_state, dense_out);
+    compute_dense(rnn->model->vad_output, vad, rnn->vad_gru_state);
+
+    for (int i = 0; i < rnn->model->input_dense_size; i++)
+        noise_input[i] = dense_out[i];
+    for (int i = 0; i < rnn->model->vad_gru_size; i++)
+        noise_input[i + rnn->model->input_dense_size] = rnn->vad_gru_state[i];
+    for (int i = 0; i < INPUT_SIZE; i++)
+        noise_input[i + rnn->model->input_dense_size + rnn->model->vad_gru_size] = input[i];
+
+    compute_gru(s, rnn->model->noise_gru, rnn->noise_gru_state, noise_input);
+
+    for (int i = 0; i < rnn->model->vad_gru_size; i++)
+        denoise_input[i] = rnn->vad_gru_state[i];
+    for (int i = 0; i < rnn->model->noise_gru_size; i++)
+        denoise_input[i + rnn->model->vad_gru_size] = rnn->noise_gru_state[i];
+    for (int i = 0; i < INPUT_SIZE; i++)
+        denoise_input[i + rnn->model->vad_gru_size + rnn->model->noise_gru_size] = input[i];
+
+    compute_gru(s, rnn->model->denoise_gru, rnn->denoise_gru_state, denoise_input);
+    compute_dense(rnn->model->denoise_output, gains, rnn->denoise_gru_state);
+}
+
+static float rnnoise_channel(AudioRNNContext *s, DenoiseState *st, float *out, const float *in)
+{
+    AVComplexFloat X[FREQ_SIZE];
+    AVComplexFloat P[WINDOW_SIZE];
+    float x[FRAME_SIZE];
+    float Ex[NB_BANDS], Ep[NB_BANDS];
+    float Exp[NB_BANDS];
+    float features[NB_FEATURES];
+    float g[NB_BANDS];
+    float gf[FREQ_SIZE];
+    float vad_prob = 0;
+    static const float a_hp[2] = {-1.99599, 0.99600};
+    static const float b_hp[2] = {-2, 1};
+    int silence;
+
+    biquad(x, st->mem_hp_x, in, b_hp, a_hp, FRAME_SIZE);
+    silence = compute_frame_features(s, st, X, P, Ex, Ep, Exp, features, x);
+
+    if (!silence) {
+        compute_rnn(s, &st->rnn, g, &vad_prob, features);
+        pitch_filter(X, P, Ex, Ep, Exp, g);
+        for (int i = 0; i < NB_BANDS; i++) {
+            float alpha = .6f;
+
+            g[i] = FFMAX(g[i], alpha * st->lastg[i]);
+            st->lastg[i] = g[i];
+        }
+
+        interp_band_gain(gf, g);
+
+        for (int i = 0; i < FREQ_SIZE; i++) {
+            X[i].re *= gf[i];
+            X[i].im *= gf[i];
+        }
+    }
+
+    frame_synthesis(s, st, out, X);
+
+    return vad_prob;
+}
+
+typedef struct ThreadData {
+    AVFrame *in, *out;
+} ThreadData;
+
+static int rnnoise_channels(AVFilterContext *ctx, void *arg, int jobnr, int nb_jobs)
+{
+    AudioRNNContext *s = ctx->priv;
+    ThreadData *td = arg;
+    AVFrame *in = td->in;
+    AVFrame *out = td->out;
+    const int start = (out->channels * jobnr) / nb_jobs;
+    const int end = (out->channels * (jobnr+1)) / nb_jobs;
+
+    for (int ch = start; ch < end; ch++) {
+        rnnoise_channel(s, &s->st[ch],
+                        (float *)out->extended_data[ch],
+                        (const float *)in->extended_data[ch]);
+    }
+
+    return 0;
+}
+
+static int filter_frame(AVFilterLink *inlink, AVFrame *in)
+{
+    AVFilterContext *ctx = inlink->dst;
+    AVFilterLink *outlink = ctx->outputs[0];
+    AVFrame *out = NULL;
+    ThreadData td;
+
+    out = ff_get_audio_buffer(outlink, FRAME_SIZE);
+    if (!out) {
+        av_frame_free(&in);
+        return AVERROR(ENOMEM);
+    }
+    out->pts = in->pts;
+
+    td.in = in; td.out = out;
+    ctx->internal->execute(ctx, rnnoise_channels, &td, NULL, FFMIN(outlink->channels,
+                                                                   ff_filter_get_nb_threads(ctx)));
+
+    av_frame_free(&in);
+    return ff_filter_frame(outlink, out);
+}
+
+static int activate(AVFilterContext *ctx)
+{
+    AVFilterLink *inlink = ctx->inputs[0];
+    AVFilterLink *outlink = ctx->outputs[0];
+    AVFrame *in = NULL;
+    int ret;
+
+    FF_FILTER_FORWARD_STATUS_BACK(outlink, inlink);
+
+    ret = ff_inlink_consume_samples(inlink, FRAME_SIZE, FRAME_SIZE, &in);
+    if (ret < 0)
+        return ret;
+
+    if (ret > 0)
+        return filter_frame(inlink, in);
+
+    FF_FILTER_FORWARD_STATUS(inlink, outlink);
+    FF_FILTER_FORWARD_WANTED(outlink, inlink);
+
+    return FFERROR_NOT_READY;
+}
+
+static av_cold int init(AVFilterContext *ctx)
+{
+    AudioRNNContext *s = ctx->priv;
+    FILE *f;
+
+    s->fdsp = avpriv_float_dsp_alloc(0);
+    if (!s->fdsp)
+        return AVERROR(ENOMEM);
+
+    if (!s->model_name)
+        return AVERROR(EINVAL);
+    f = av_fopen_utf8(s->model_name, "r");
+    if (!f)
+        return AVERROR(EINVAL);
+
+    s->model = rnnoise_model_from_file(f);
+    fclose(f);
+    if (!s->model)
+        return AVERROR(EINVAL);
+
+    for (int i = 0; i < FRAME_SIZE; i++) {
+        s->window[i] = sin(.5*M_PI*sin(.5*M_PI*(i+.5)/FRAME_SIZE) * sin(.5*M_PI*(i+.5)/FRAME_SIZE));
+        s->window[WINDOW_SIZE - 1 - i] = s->window[i];
+    }
+
+    for (int i = 0; i < NB_BANDS; i++) {
+        for (int j = 0; j < NB_BANDS; j++) {
+            s->dct_table[i*NB_BANDS + j] = cosf((i + .5f) * j * M_PI / NB_BANDS);
+            if (j == 0)
+                s->dct_table[i*NB_BANDS + j] *= sqrtf(.5);
+        }
+    }
+
+    return 0;
+}
+
+static av_cold void uninit(AVFilterContext *ctx)
+{
+    AudioRNNContext *s = ctx->priv;
+
+    av_freep(&s->fdsp);
+    rnnoise_model_free(s->model);
+    s->model = NULL;
+
+    if (s->st) {
+        for (int ch = 0; ch < s->channels; ch++) {
+            av_freep(&s->st[ch].rnn.vad_gru_state);
+            av_freep(&s->st[ch].rnn.noise_gru_state);
+            av_freep(&s->st[ch].rnn.denoise_gru_state);
+            av_tx_uninit(&s->st[ch].tx);
+            av_tx_uninit(&s->st[ch].txi);
+        }
+    }
+    av_freep(&s->st);
+}
+
+static const AVFilterPad inputs[] = {
+    {
+        .name         = "default",
+        .type         = AVMEDIA_TYPE_AUDIO,
+        .config_props = config_input,
+    },
+    { NULL }
+};
+
+static const AVFilterPad outputs[] = {
+    {
+        .name          = "default",
+        .type          = AVMEDIA_TYPE_AUDIO,
+    },
+    { NULL }
+};
+
+#define OFFSET(x) offsetof(AudioRNNContext, x)
+#define AF AV_OPT_FLAG_AUDIO_PARAM|AV_OPT_FLAG_FILTERING_PARAM
+
+static const AVOption arnndn_options[] = {
+    { "model", "set model name", OFFSET(model_name), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, AF },
+    { "m",     "set model name", OFFSET(model_name), AV_OPT_TYPE_STRING, {.str=NULL}, 0, 0, AF },
+    { NULL }
+};
+
+AVFILTER_DEFINE_CLASS(arnndn);
+
+AVFilter ff_af_arnndn = {
+    .name          = "arnndn",
+    .description   = NULL_IF_CONFIG_SMALL("Reduce noise from speech using Recurrent Neural Networks."),
+    .query_formats = query_formats,
+    .priv_size     = sizeof(AudioRNNContext),
+    .priv_class    = &arnndn_class,
+    .activate      = activate,
+    .init          = init,
+    .uninit        = uninit,
+    .inputs        = inputs,
+    .outputs       = outputs,
+    .flags         = AVFILTER_FLAG_SLICE_THREADS,
+};
diff --git a/libavfilter/allfilters.c b/libavfilter/allfilters.c
index 4f8b3039ed..a7a165e0d8 100644
--- a/libavfilter/allfilters.c
+++ b/libavfilter/allfilters.c
@@ -65,6 +65,7 @@ extern AVFilter ff_af_apulsator;
 extern AVFilter ff_af_arealtime;
 extern AVFilter ff_af_aresample;
 extern AVFilter ff_af_areverse;
+extern AVFilter ff_af_arnndn;
 extern AVFilter ff_af_aselect;
 extern AVFilter ff_af_asendcmd;
 extern AVFilter ff_af_asetnsamples;
diff --git a/libavfilter/version.h b/libavfilter/version.h
index 901fae095b..e8c5f4ab61 100644
--- a/libavfilter/version.h
+++ b/libavfilter/version.h
@@ -30,8 +30,8 @@
 #include "libavutil/version.h"
 
 #define LIBAVFILTER_VERSION_MAJOR   7
-#define LIBAVFILTER_VERSION_MINOR  62
-#define LIBAVFILTER_VERSION_MICRO 101
+#define LIBAVFILTER_VERSION_MINOR  63
+#define LIBAVFILTER_VERSION_MICRO 100
 
 
 #define LIBAVFILTER_VERSION_INT AV_VERSION_INT(LIBAVFILTER_VERSION_MAJOR, \