Independent Deeply Learned Matrix Analysis for Determined Audio Source Separation

Abstract

In this paper, we propose a new framework called independent deeply learned matrix analysis (IDLMA), which unifies a deep neural network (DNN) and independence-based multichannel audio source separation. IDLMA utilizes both pretrained DNN source models and statistical independence between sources for the separation, where the time-frequency structures of each source are iteratively optimized by a DNN while enhancing the estimation accuracy of the spatial demixing filters. As the source generative model, we introduce a complex heavy-tailed distribution to improve the separation performance. In addition, we address a semi-supervised situation; namely, a solo-recorded audio dataset can be prepared for only one source in the mixture signal. To solve the limited-data problem, we propose an appropriate data augmentation method to adapt the DNN source models to the observed signal, which enables IDLMA to work even in the semi-supervised situation. Experiments are conducted using music signals with a training dataset in both supervised and semi-supervised situations. The results show the validity of the proposed method in terms of the separation accuracy.

Consistent independent low-rank matrix analysis for determined blind source separation

“…Its combination with stateof-the-art models, including ILRMA, is of great interest because the current mainstream algorithm for determined audio source separation is centered on ILRMA, which is based on an NMF-based richer time-frequency source model. Indeed, many recent papers are based on the framework of ILRMA [17][18][19][20][21][22][23][24][25][26][27][28][29]. Even though combining ILRMA with the spectrogram consistency should be able to exceed the limit of existing BSS algorithms, no such method has been investigated in the literature.…”

“…where e n ∈ {0, 1} N is the unit vector with the nth element equal to unity. Update rules (19)-(24) ensure the monotonic non-increase of the negative log-likelihood function L. After iterative calculations of updates (19)- (24), the separated signal can be obtained by (12). Equation 22 is equivalent to beamforming [53] to x ij with the beamformer coefficients w in .…”

“…That is, STFT ω (ISTFT ω (Y n )) performs nothing if Y n is consistent, but otherwise, it smooths the complex spectrogram Y n , by going through the time domain, so that the uncertainty principle is satisfied. In Consistent ILRMA, the calculation of (25) is performed in each iteration of parameter updates based on (19)- (24). Enforcing the spectrogram consistency for the temporary separated signal Y n in each iteration guides the parameters W i , T n , and V n to better solutions, which results in higher separation performance compared to that of conventional ILRMA.…”

“…This extension has greatly improved the performance of separation by taking the low-rank time-frequency structure (co-occurrence among the time-frequency bins) of the source signals into account. ILRMA has achieved the state-of-the-art performance and been further developed by several researchers [17][18][19][20][21][22][23][24][25][26][27][28][29]. In this respect, ILRMA can be considered the new standard of the determined BSS algorithms.…”

Consistent Independent Low-Rank Matrix Analysis for Determined Blind Source Separation

“…This extension has greatly improved the performance of separation by taking the low-rank time-frequency structure (co-occurrence among the time-frequency bins) of the source signals into account. ILRMA has achieved the state-of-the-art performance and been further developed by several researchers [17][18][19][20][21][22][23][24]. In this respect, ILRMA can be considered as the new standard of the determined BSS algorithms.…”