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Dual transform based joint learning single channel speech separation using generative joint dictionary learning
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DOI：10.1007/s11042-022-12816-0.png)
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Single channel speech separation (SS) is highly significant in many real-world speech processing applications such as hearing aids, automatic speech recognition, control humanoid robots, and cocktail-party issues. The performance of the SS is crucial for these applications, but better accuracy has yet to be developed. Some researchers have tried to separate speech using only the magnitude part, and some are tried to solve complex domains. We propose a dual transform SS method that serially uses the dual-tree complex wavelet transform (DTCWT) and short-term Fourier transform (STFT), and jointly learns the magnitude, real and imaginary parts of the signal applying a generative joint dictionary learning (GJDL). At first, the time-domain speech signal is decomposed by DTCWT, which produces a set of subband signals. Then STFT is connected to each subband signal, which converts each subband signal to the time-frequency domain and builds a complex spectrogram that prepares three parts like real, imaginary and magnitude for each subband signal. Next, we utilize the GJDL approach for making the joint dictionaries, and then the batch least angle regression with a coherence criterion (LARC) algorithm is used for sparse coding. Afterward, computes the initially estimated signals in two different ways, one by considering only the magnitude part and another by considering real and imaginary components. Finally, we apply the Gini index (GI) to the initially estimated signals to achieve better accuracy. The proposed algorithm demonstrates the best performance in all considered evaluation metrics compared to the mentioned algorithms.
Keyword:
Speech separation (SS)
Dual-tree complex wavelet transform (DTCWT)
Generative joint dictionary learning (GJDL)
Short-time Fourier transform (STFT)
Gini index (GI)
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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