Deep learning for monaural speech separation
Monaural source separation is useful for many real-world applications though it is a challenging problem. In this paper, we study deep learning for monaural speech separation. We propose the joint optimization of the deep learning models (deep neural networks and recurrent neural networks) with an extra masking layer, which enforces a reconstruction constraint. Moreover, we explore a discriminative training criterion for the neural networks to further enhance the separation performance. We evaluate our approaches using the TIMIT speech corpus for a monaural speech separation task. Our proposed models achieve about 3.8~4.9 dB SIR gain compared to NMF models, while maintaining better SDRs and SARs.
Code (1)
Tasks
Deep LearningMulti-Speaker Source SeparationSpeech SeparationSimilar Papers 제목 키워드 기반
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation
Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, an infinite number of solutions are possi…
DenoisingSpeech DenoisingSpeech SeparationMonaural Multi-Speaker Speech Separation Using Efficient Transformer Model
Cocktail party problem is the scenario where it is difficult to separate or distinguish individual speaker from a mixed speech from several speakers. There have been several researches going on in this field but the size…
Computational EfficiencySpeech SeparationEnd-to-End Monaural Multi-speaker ASR System without Pretraining
Recently, end-to-end models have become a popular approach as an alternative to traditional hybrid models in automatic speech recognition (ASR). The multi-speaker speech separation and recognition task is a central task …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech Recognition+1Deep neural network techniques for monaural speech enhancement: state of the art analysis
Deep neural networks (DNN) techniques have become pervasive in domains such as natural language processing and computer vision. They have achieved great success in these domains in task such as machine translation and im…
Art AnalysisImage GenerationMachine TranslationSpeaker Separation+2Monaural source separation: From anechoic to reverberant environments
Impressive progress in neural network-based single-channel speech source separation has been made in recent years. But those improvements have been mostly reported on anechoic data, a situation that is hardly met in prac…