Deep Attractor Network (DANet) is the state-of-the-art technique in speech separation field, which uses Bidirectional Long Short-Term Memory (BLSTM), but the complexity of the DANet model is very high. In this paper, a simplified and powerful DANet model is proposed using Bidirectional Gated neural network (BGRU) instead of BLSTM. The Gaussian Mixture Model (GMM) other than the k-means was applied in DANet as a clustering algorithm to reduce the complexity and increase the learning speed and accuracy. The metrics used in this paper are Signal to Distortion Ratio (SDR), Signal to Interference Ratio (SIR), Signal to Artifact Ratio (SAR), and Perceptual Evaluation Speech Quality (PESQ) score. Two speaker mixture datasets from TIMIT corpus were prepared to evaluate the proposed model, and the system achieved 12.3 dB and 2.94 for SDR and PESQ scores respectively, which were better than the original DANet model. Other improvements were 20.7% and 17.9% in the number of parameters and time training, respectively. The model was applied on mixed Arabic speech signals and the results were better than that in English.

This paper examines the applicability in realistic scenari os of two deep learning based solutions to the overlapping speake r separation problem. Firstly, we present experiments that s how that these methods are applicable for a broad range of languages. Further experimentation indicates limited perfor mance loss for untrained languages, when these have common features with the trained language(s). Secondly, it investiga tes how the methods deal with realistic background noise and propos es some modifications to better cope with these disturbances. T he deep learning methods that will be examined are deep cluster ing and deep attractor networks.

ABSTRACT Isolating individual instruments in a musical mixture has a myriad of potential applications, and seems imminently achievable given the levels of performance reached by recent deep learning methods. While most musical source separation techniques learn an independent model for each instrument, we propose using a common embedding space for the time-frequency bins of all instruments in a mixture inspired by deep clustering and deep attractor networks. Additionally, an auxiliary network is used to generate parameters of a Gaussian mixture model (GMM) where the posterior distribution over GMM components in the embedding space can be used to create a mask that separates individual sources from a mixture. In addition to outperforming a mask-inference baseline on the MUSDB-18 dataset, our embedding space is easily interpretable and can be used for query-based separation. Index Terms -- source separation, deep clustering, music, classification, neural networks 1. INTRODUCTION Audio source separation is the act of isolating sound-producing sources in an auditory scene. Examples include separating singing voice from accompanying music, the voice of a single speaker at a crowded party, or the sound of a car backfiring in a loud urban soundscape.