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• W2892365986 abstract "Robust speech processing in multitalker acoustic environments requiresautomatic speech separation. While single-channel, speaker-independent speechseparation methods have recently seen great progress, the accuracy, latency,and computational cost of speech separation remain insufficient. The majorityof the previous methods have formulated the separation problem through thetime-frequency representation of the mixed signal, which has several drawbacks,including the decoupling of the phase and magnitude of the signal, thesuboptimality of spectrogram representations for speech separation, and thelong latency in calculating the spectrogram. To address these shortcomings, wepropose the time-domain audio separation network (TasNet), which is a deeplearning autoencoder framework for time-domain speech separation. TasNet uses aconvolutional encoder to create a representation of the signal that isoptimized for extracting individual speakers. Speaker extraction is achieved byapplying a weighting function (mask) to the encoder output. The modifiedencoder representation is then inverted to the sound waveform using a lineardecoder. The masks are found using a temporal convolutional network consistingof dilated convolutions, which allow the network to model the long-termdependencies of the speech signal. This end-to-end speech separation algorithmsignificantly outperforms previous time-frequency methods in terms ofseparating speakers in mixed audio, even when compared to the separationaccuracy achieved with the ideal time-frequency mask of the speakers. Inaddition, TasNet has a smaller model size and a shorter minimum latency, makingit a suitable solution for both offline and real-time speech separationapplications. This study therefore represents a major step toward actualizingspeech separation for real-world speech processing technologies." @default.
• W2892365986 created "2018-09-27" @default.
• W2892365986 creator A5033351155 @default.
• W2892365986 creator A5048439332 @default.
• W2892365986 date "2018-09-20" @default.
• W2892365986 modified "2023-10-17" @default.
• W2892365986 title "TasNet: Surpassing Ideal Time-Frequency Masking for Speech Separation." @default.
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