4,024 research outputs found
Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair
Time-frequency masking or spectrum prediction computed via short symmetric
windows are commonly used in low-latency deep neural network (DNN) based source
separation. In this paper, we propose the usage of an asymmetric
analysis-synthesis window pair which allows for training with targets with
better frequency resolution, while retaining the low-latency during inference
suitable for real-time speech enhancement or assisted hearing applications. In
order to assess our approach across various model types and datasets, we
evaluate it with both speaker-independent deep clustering (DC) model and a
speaker-dependent mask inference (MI) model. We report an improvement in
separation performance of up to 1.5 dB in terms of source-to-distortion ratio
(SDR) while maintaining an algorithmic latency of 8 ms.Comment: Accepted to EUSIPCO-202
TasNet: time-domain audio separation network for real-time, single-channel speech separation
Robust speech processing in multi-talker environments requires effective
speech separation. Recent deep learning systems have made significant progress
toward solving this problem, yet it remains challenging particularly in
real-time, short latency applications. Most methods attempt to construct a mask
for each source in time-frequency representation of the mixture signal which is
not necessarily an optimal representation for speech separation. In addition,
time-frequency decomposition results in inherent problems such as
phase/magnitude decoupling and long time window which is required to achieve
sufficient frequency resolution. We propose Time-domain Audio Separation
Network (TasNet) to overcome these limitations. We directly model the signal in
the time-domain using an encoder-decoder framework and perform the source
separation on nonnegative encoder outputs. This method removes the frequency
decomposition step and reduces the separation problem to estimation of source
masks on encoder outputs which is then synthesized by the decoder. Our system
outperforms the current state-of-the-art causal and noncausal speech separation
algorithms, reduces the computational cost of speech separation, and
significantly reduces the minimum required latency of the output. This makes
TasNet suitable for applications where low-power, real-time implementation is
desirable such as in hearable and telecommunication devices.Comment: Camera ready version for ICASSP 2018, Calgary, Canad
Online Speaker Separation Using Deep Clustering
In this thesis, a low-latency variant of speaker-independent deep clustering method is
proposed for speaker separation. Compared to the offline deep clustering separation
system, bidirectional long-short term memory networks (BLSTMs) are replaced with
long-short term memory networks (LSTMs). The reason is that the data has to be
fed to the BLSTM networks both forward and backward directions. Additionally, the
final outputs depend on both directions, which make online processing not possible.
Also, 32 ms synthesis window is replaced with 8 ms in order to cooperate with low-
latency applications like hearing aids since the algorithmic latency depends upon the
length of synthesis window. Furthermore, the beginning of the audio mixture, here,
referred as buffer, is used to get the cluster centers for the constituent speakers in the
mixture serving as the initialization purpose. Later, those centers are used to assign
clusters for the rest of the mixture to achieve speaker separation with the latency
of 8 ms. The algorithm is evaluated on the Wall Street Journal corpus (WSJ0).
Changing the networks from BLSTM to LSTM while keeping the same window
length degrades the separation performance measured by signal-to-distortion ratio
(SDR) by 1.0 dB, which implies that the future information is important for the
separation. For investigating the effect of window length, keeping the same network
structure (LSTM), by changing window length from 32 ms to 8 ms, another 1.1 dB
drop in SDR is found. For the low-latency deep clustering speaker separation system,
different duration of buffer is studied. It is observed that initially, the separation
performance increases as the buffer increases. However, with buffer length of 0.3 s,
the separation performance keeps steady even by increasing the buffer. Compared to
offline deep clustering separation system, degradation of 2.8 dB in SDR is observed
for online system
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