Dolby audio ai workshop speech coding - cong zhou

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This document discusses SampleRNN, a neural audio generation model that can generate high-quality speech. SampleRNN uses a multi-rate recurrent neural network architecture with learned upsampling to directly model raw audio waveforms. It can be conditioned on vocoder features to allow for high-quality speech coding at bitrates as low as 6.4 kbps, significantly lower than traditional speech codecs. Experimental results show SampleRNN achieves better quality than existing codecs like AMR-WB at comparable or lower bitrates. Future work may focus on improving SampleRNN's robustness and reducing its computational complexity.

HIGH QUALITY SPEECH CODING USING
SAMPLERNN
Cong Zhou
Dolby Laboratories

2
Entropy
Coding
Vocoder
Analysis
Speech Bitstream
Encoder
Vocoder
Synthesis
SpeechEntropy
Decoding
Decoder
SPARK JOY
Inspired from Video Coding presentation by Anne Aaron, Director of Video Algorithms at Netflix

© 2019 DOLBY LABORATORIES, INC.
Raw Audio Generative Models
• Sequential generative models
o Directly estimate waveform distributions
𝑝 𝑋 = $
%&'
()*
𝑝 𝑥%,* 𝑥*, . . . , 𝑥%
o Breakthrough success in generating realistic speech
o WaveNet [1], SampleRNN [2], WaveRNN [3]
3
[1] Oord, Aaron van den, et al. "Wavenet: A generative model for raw audio." arXiv preprint arXiv:1609.03499 (2016).
[2] Mehri, Soroush, et al. "SampleRNN: An unconditional end-to-end neural audio generation model." arXiv preprint
arXiv:1612.07837 (2016).
[3] Kalchbrenner, Nal, et al. "Efficient neural audio synthesis." arXiv preprint arXiv:1802.08435 (2018).

© 2019 DOLBY LABORATORIES, INC.
SampleRNN
4
SampleRNN: multi-rate RNN based generative model (MILA)

© 2019 DOLBY LABORATORIES, INC.
GRU
Learned upsampling
+
1 ⇥ 1
conv
1 ⇥ 1
conv
Tier 2
GRU
Learned upsampling
+
1 ⇥ 1
conv
1 ⇥ 1
conv
Tier 3
GRU
Learned upsampling
+
1 ⇥ 1
conv
1 ⇥ 1
conv
Tier 4
1 ⇥ 1
conv
MLP Tier 1
ht
p(xi|x<i, ht)
xi FS(2), . . . , xi 1
xi FS(3), . . . , xi 1
xi FS(4), . . . , xi 1
xi FS(1), . . . , xi 1
5
SampleRNN with conditioning

© 2019 DOLBY LABORATORIES, INC.
Training conditional SampleRNN
6
SampleRNN
Speech
16 kHz
Vocoder
Analysis [1]
Speech
16 kHz 𝐡 𝒕
conditioning info:
LPC filter, RMS level of LPC residual,
pitch, voicing level
E{-log p(x)}
[1] Per Hedelin, “A sinusoidal LPC vocoder,” in 2000 IEEE Workshop on Speech Coding. Proceedings. Meeting the
Challenges of the New Millennium (Cat. No.00EX421), Sept 2000, pp. 2–4

© 2019 DOLBY LABORATORIES, INC.
Coding Scheme
7
DECODER
sampleRNN
Speech
16 kHz
ENCODER
Entropy
Coding
Vocoder
Analysis
Entropy
Decoding
Speech
16 kHz
Bitstream 𝐡 𝒕
High-quality speech coding with SampleRNN
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes ICASSP 2019
Quantized vocoder features

BadPoorFairGoodExcellent
MUSHRAScore
High-quality speech coding with SampleRNN
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes ICASSP 2019
23.05 kbps 16 kbps 6.4 kbps

© 2019 DOLBY LABORATORIES, INC.
Reference
(original)
AMR-WB
(23.05 kbps)
SILK
(16 kbps)
sRNN based
(6.4 kbps)
High-quality speech coding with SampleRNN
Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes ICASSP 2019
Demo

© 2019 DOLBY LABORATORIES, INC.
Future directions
• Robustness
• Low complexity
10

© 2019 DOLBY LABORATORIES, INC.
Recent related work
"WaveNet based low rate speech coding." Kleijn, W. Bastiaan, et al. 2018 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018.
"LPCNet: Improving neural speech synthesis through linear prediction." Valin, Jean-Marc, and Jan
Skoglund. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.
"Low bit-rate speech coding with VQ-VAE and a WaveNet decoder." Gârbacea, Cristina, et al. 2019 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.
"A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet." Valin, Jean-Marc, and Jan Skoglund.
arXiv preprint arXiv:1903.12087 (2019).
"GELP: GAN-Excited Liner Prediction for Speech Synthesis from Mel-spectrogram." Juvela, Lauri, et al.
arXiv preprint arXiv:1904.03976 (2019).
11

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1. HIGH QUALITY SPEECH CODING USING SAMPLERNN Cong Zhou Dolby Laboratories

2. 2 Entropy Coding Vocoder Analysis Speech Bitstream Encoder Vocoder Synthesis SpeechEntropy Decoding Decoder SPARK JOY Inspired from Video Coding presentation by Anne Aaron, Director of Video Algorithms at Netflix

3. © 2019 DOLBY LABORATORIES, INC. Raw Audio Generative Models • Sequential generative models o Directly estimate waveform distributions 𝑝 𝑋 = $ %&' ()* 𝑝 𝑥%,* 𝑥*, . . . , 𝑥% o Breakthrough success in generating realistic speech o WaveNet [1], SampleRNN [2], WaveRNN [3] 3 [1] Oord, Aaron van den, et al. "Wavenet: A generative model for raw audio." arXiv preprint arXiv:1609.03499 (2016). [2] Mehri, Soroush, et al. "SampleRNN: An unconditional end-to-end neural audio generation model." arXiv preprint arXiv:1612.07837 (2016). [3] Kalchbrenner, Nal, et al. "Efficient neural audio synthesis." arXiv preprint arXiv:1802.08435 (2018).

5. © 2019 DOLBY LABORATORIES, INC. GRU Learned upsampling + 1 ⇥ 1 conv 1 ⇥ 1 conv Tier 2 GRU Learned upsampling + 1 ⇥ 1 conv 1 ⇥ 1 conv Tier 3 GRU Learned upsampling + 1 ⇥ 1 conv 1 ⇥ 1 conv Tier 4 1 ⇥ 1 conv MLP Tier 1 ht p(xi|x<i, ht) xi FS(2), . . . , xi 1 xi FS(3), . . . , xi 1 xi FS(4), . . . , xi 1 xi FS(1), . . . , xi 1 5 SampleRNN with conditioning

6. © 2019 DOLBY LABORATORIES, INC. Training conditional SampleRNN 6 SampleRNN Speech 16 kHz Vocoder Analysis [1] Speech 16 kHz 𝐡 𝒕 conditioning info: LPC filter, RMS level of LPC residual, pitch, voicing level E{-log p(x)} [1] Per Hedelin, “A sinusoidal LPC vocoder,” in 2000 IEEE Workshop on Speech Coding. Proceedings. Meeting the Challenges of the New Millennium (Cat. No.00EX421), Sept 2000, pp. 2–4

7. © 2019 DOLBY LABORATORIES, INC. Coding Scheme 7 DECODER sampleRNN Speech 16 kHz ENCODER Entropy Coding Vocoder Analysis Entropy Decoding Speech 16 kHz Bitstream 𝐡 𝒕 High-quality speech coding with SampleRNN Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes ICASSP 2019 Quantized vocoder features

8. BadPoorFairGoodExcellent MUSHRAScore High-quality speech coding with SampleRNN Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes ICASSP 2019 23.05 kbps 16 kbps 6.4 kbps

9. © 2019 DOLBY LABORATORIES, INC. Reference (original) AMR-WB (23.05 kbps) SILK (16 kbps) sRNN based (6.4 kbps) High-quality speech coding with SampleRNN Janusz Klejsa, Per Hedelin, Cong Zhou, Roy Fejgin, Lars Villemoes ICASSP 2019 Demo

11. © 2019 DOLBY LABORATORIES, INC. Recent related work "WaveNet based low rate speech coding." Kleijn, W. Bastiaan, et al. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. "LPCNet: Improving neural speech synthesis through linear prediction." Valin, Jean-Marc, and Jan Skoglund. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. "Low bit-rate speech coding with VQ-VAE and a WaveNet decoder." Gârbacea, Cristina, et al. 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. "A Real-Time Wideband Neural Vocoder at 1.6 kb/s Using LPCNet." Valin, Jean-Marc, and Jan Skoglund. arXiv preprint arXiv:1903.12087 (2019). "GELP: GAN-Excited Liner Prediction for Speech Synthesis from Mel-spectrogram." Juvela, Lauri, et al. arXiv preprint arXiv:1904.03976 (2019). 11

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