在混响环境中分离竞争语音需要模型在保持分离效率的同时保留空间线索。我们提出了使用八个麦克风的相位感知耳条件语音分离网络（PEASE-8），该网络接收复数STFT，并直接将原始STFT输入引入到早期解码器层，绕过整个编码器路径以改善重建效果。该模型基于SI-SDR目标进行端到端训练，以直达路径耳信号为目标，对固定方位角上的两名说话人联合执行分离和去混响，无需置换不变训练。在涵盖无混响、混响和噪声条件的空间化双说话人混合语音上，PEASE-8表现出强大的分离能力和可懂度。在混响环境中，当T60 = 0.6 s时，它实现了12.37 dB SI-SDR、0.87 STOI和1.86 PESQ，同时在无混响条件下仍保持竞争力。
英文摘要：
Separating competing speech in reverberant environments requires models that preserve spatial cues while maintaining separation efficiency. We present a Phase-aware Ear-conditioned speaker Separation network using eight microphones (PEASE-8) that consumes complex STFTs and directly introduces a raw-STFT input to the early decoder layer, bypassing the entire encoder pathway to improve reconstruction. The model is trained end-to-end with an SI-SDR-based objective against direct-path ear targets, jointly performing separation and dereverberation for two speakers in a fixed azimuth, eliminating the need for permutation invariant training. On spatialized two-speaker mixtures spanning anechoic, reverberant, and noisy conditions, PEASE-8 delivers strong separation and intelligibility. In reverberant environments, it achieves 12.37 dB SI-SDR, 0.87 STOI, and 1.86 PESQ at T60 = 0.6 s, while remaining competitive under anechoic conditions.
The Ohio State University（俄亥俄州立大学）；Facebook Reality Labs（Facebook现实实验室）；Facebook AI Research（Facebook人工智能研究院）；Tel-Aviv University（特拉维夫大学）
Most existing deep learning based binaural speaker separation systems focus on producing a monaural estimate for each of the target speakers, and thus do not preserve the interaural cues, which are crucial for human listeners to perform sound localization and lateralization. In this study, we address talker-independent binaural speaker separation with interaural cues preserved in the estimated binaural signals. Specifically, we extend a newly-developed gated recurrent neural network for monaural separation by additionally incorporating self-attention mechanisms and dense connectivity. We devel…
Binaural speech separation in real-world scenarios often involves moving speakers. Most current speech separation methods use utterance-level permutation invariant training (u-PIT) for training. In inference time, however, the order of outputs can be inconsistent over time particularly in long-form speech separation. This situation which is referred to as the speaker swap problem is even more problematic when speakers constantly move in space and therefore poses a challenge for consistent placement of speakers in output channels. Here, we describe a real-time binaural speech separation model b…
BiEAR: A Human Auditory-Inspired Adaptive Binaural Front-end for Multi-Speaker Localisation and Distance Estimation
The University of New South Wales（新南威尔士大学）；Tongyi Speech Lab, Alibaba Group（通义语音实验室，阿里巴巴集团）；School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen（人工智能学院，香港中文大学（深圳））
Multi-Speaker DOA Estimation in Binaural Hearing Aids using Deep Learning and Speaker Count Fusion
School of Electrical Engineering and Computer Science, University of Ottawa, Canada（Ottawa大学电气与计算机科学学院, 加拿大）；Department of Electrical and Computer Engineering, Universite de Sherbrooke, Canada（Sherbrooke大学电气与计算机工程系, 加拿大）；WS Audiology, Germany（德国WS听觉学）
AuralNet: Hierarchical Attention-based 3D Binaural Localization of Overlapping Speakers
Southern University of Science and Technology (SUSTech)（南方科技大学）；China SUSTechChina（中国南方科技大学）；City University of Hong Kong（香港城市大学）；School of Automation and Intelligent Manufacturing（自动化与智能制造学院）；Department of Computer Science and Engineering（计算机科学与工程系）；Department of Mechanical Engineering（机械工程系）
End-to-end multi-channel speaker extraction and binaural speech synthesis
Laboratory of Noise and Audio Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China（声学研究所噪声与音频研究实验室，中国科学院，北京，中国）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）；Communication University of China, Beijing, China（中国传媒大学，北京，中国）；Tianjin 712 Communication & Broadcasting Co., Ltd.（天津712通信广播有限公司）；Tianjin Tianan Borui Technology Co., Ltd.（天津天安伯瑞科技有限公司）
In this paper, a binaural beamforming algorithm for hearing aid applications is introduced.The beamforming algorithm is designed to be robust to some error in the estimate of the target speaker direction. The algorithm has two main components: a robust target linearly constrained minimum variance (TLCMV) algorithm based on imposing two constraints around the estimated direction of the target signal, and a post-processor to help with the preservation of binaural cues. The robust TLCMV provides a good level of noise reduction and low level of target distortion under realistic conditions. The pos…
Deep learning speech separation algorithms have achieved great success in improving the quality and intelligibility of separated speech from mixed audio. Most previous methods focused on generating a single-channel output for each of the target speakers, hence discarding the spatial cues needed for the localization of sound sources in space. However, preserving the spatial information is important in many applications that aim to accurately render the acoustic scene such as in hearing aids and augmented reality (AR). Here, we propose a speech separation algorithm that preserves the interaural …
Facebook AI Research（脸书人工智能研究院）；Facebook Reality Labs（脸书现实实验室）；University of Maryland（马里兰大学）
Deep neural networks have recently shown great success in the task of blind source separation, both under monaural and binaural settings. Although these methods were shown to produce high-quality separations, they were mainly applied under offline settings, in which the model has access to the full input signal while separating the signal. In this study, we convert a non-causal state-of-the-art separation model into a causal and real-time model and evaluate its performance under both online and offline settings. We compare the performance of the proposed model to several baseline methods under…
Auditory perception involves cues in the monaural auditory pathways as well as binaural cues based on differences between the ears. So far auditory models have often focused on either monaural or binaural experiments in isolation. Although binaural models typically build upon stages of (existing) monaural models, only a few attempts have been made to extend a monaural model by a binaural stage using a unified decision stage for monaural and binaural cues. In such approaches, a typical prototype of binaural processing has been the classical equalization-cancelation mechanism, which either invol…
University of Surrey（萨里大学）；Qingdao University of Science and Technology（青岛科技大学）
Typical methods for binaural source separation consider only the direct sound as the target signal in a mixture. However, in most scenarios, this assumption limits the source separation performance. It is well known that the early reflections interact with the direct sound, producing acoustic effects at the listening position, e.g. the so-called comb filter effect. In this article, we propose a novel source separation model, that utilizes both the direct sound and the first early reflection information to model the comb filter effect. This is done by observing the interaural phase difference o…
STOI-optimal masking has been previously proposed and developed for single-channel speech enhancement. In this paper, we consider the extension to the task of binaural speech enhancement in which spatial information is known to be important to speech understanding and therefore should be preserved by the enhancement processing. Masks are estimated for each of the binaural channels individually and a `better-ear listening' mask is computed by choosing the maximum of the two masks. The estimated mask is used to supply probability information about the speech presence in each time-frequency bin t…
ABSE-NET: A Lightweight Neural Model for Active Binaural Speech Enhancement in Open-Fit Hearing Aids
College of Computer Science, Inner Mongolia University（内蒙古大学计算机学院）；College of Electronic and Information Engineering, Inner Mongolia University（内蒙古大学电子与信息工程学院）
University of Science and Technology of China（中国科学技术大学）；Imperial College London（帝国理工学院）；Microsoft Research Asia（微软亚洲研究院）；Microsoft Azure Speech（微软Azure语音部门）；University of Surrey（萨里大学）；South China University of Technology（华南理工大学）
Binaural audio plays a significant role in constructing immersive augmented and virtual realities. As it is expensive to record binaural audio from the real world, synthesizing them from mono audio has attracted increasing attention. This synthesis process involves not only the basic physical warping of the mono audio, but also room reverberations and head/ear related filtrations, which, however, are difficult to accurately simulate in traditional digital signal processing. In this paper, we formulate the synthesis process from a different perspective by decomposing the binaural audio into a c…
Texas A&M University（德州农工大学）；New Jersey Institute of Technology（新泽西理工学院）；Temple University（天普大学）；University of Dayton（代顿大学）；Rutgers University（罗格斯大学）
Eavesdropping from the user's smartphone is a well-known threat to the user's safety and privacy. Existing studies show that loudspeaker reverberation can inject speech into motion sensor readings, leading to speech eavesdropping. While more devastating attacks on ear speakers, which produce much smaller scale vibrations, were believed impossible to eavesdrop with zero-permission motion sensors. In this work, we revisit this important line of reach. We explore recent trends in smartphone manufacturers that include extra/powerful speakers in place of small ear speakers, and demonstrate the feas…
Speaker recognition performance has been greatly improved with the emergence of deep learning. Deep neural networks show the capacity to effectively deal with impacts of noise and reverberation, making them attractive to far-field speaker recognition systems. The x-vector framework is a popular choice for generating speaker embeddings in recent literature due to its robust training mechanism and excellent performance in various test sets. In this paper, we start with early work on including invariant representation learning (IRL) to the loss function and modify the approach with centroid align…
Tianjin University（天津大学）；Nanyang Technological University（南洋理工大学）；Japan Advanced Institute of Science and Technology（日本北陆先端科学技术大学院大学）；National University of Singapore（新加坡国立大学）；University of Bremen（不来梅大学）
Speaker extraction requires a sample speech from the target speaker as the reference. However, enrolling a speaker with a long speech is not practical. We propose a speaker extraction technique, that performs in multiple stages to take full advantage of short reference speech sample. The extracted speech in early stages is used as the reference speech for late stages. For the first time, we use frame-level sequential speech embedding as the reference for target speaker. This is a departure from the traditional utterance-based speaker embedding reference. In addition, a signal fusion scheme is …
Institute of Acoustics, CAS（中国科学院声学研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Deep attractor networks (DANs) perform speech separation with discriminative embeddings and speaker attractors. Compared with methods based on the permutation invariant training (PIT), DANs define a deep embedding space and deliver a more elaborate representation on each time-frequency (T-F) bin. However, it has been observed that the DANs achieve limited improvement on the signal quality if directly deployed in a reverberant environment. Following the success of time-domain separation networks on the clean mixture speech, we propose a time-domain DAN (TD-DAN) with two-streams of convolutional…
Brno University of Technology（布尔诺理工大学）；NTT Corporation（日本电信电话株式会社）
In typical multi-talker speech recognition systems, a neural network-based acoustic model predicts senone state posteriors for each speaker. These are later used by a single-talker decoder which is applied on each speaker-specific output stream separately. In this work, we argue that such a scheme is sub-optimal and propose a principled solution that decodes all speakers jointly. We modify the acoustic model to predict joint state posteriors for all speakers, enabling the network to express uncertainty about the attribution of parts of the speech signal to the speakers. We employ a joint decod…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Columbia University（哥伦比亚大学）
Most speech separation methods, trying to separate all channel sources simultaneously, are still far from having enough general- ization capabilities for real scenarios where the number of input sounds is usually uncertain and even dynamic. In this work, we employ ideas from auditory attention with two ears and propose a speaker and direction inferred speech separation network (dubbed SDNet) to solve the cocktail party problem. Specifically, our SDNet first parses out the respective perceptual representations with their speaker and direction characteristics from the mixture of the scene in a s…
Although deep-learning-based methods have markedly improved the performance of speech separation over the past few years, it remains an open question how to integrate multi-channel signals for speech separation. We propose two methods, namely, early-fusion and late-fusion methods, to integrate multi-channel information based on the time-domain audio separation network, which has been proven effective in single-channel speech separation. We also propose channel-sequential-transfer learning, which is a transfer learning framework that applies the parameters trained for a lower-channel network as…
With its strong modeling capacity that comes from a multi-head and multi-layer structure, Transformer is a very powerful model for learning a sequential representation and has been successfully applied to speech separation recently. However, multi-channel speech separation sometimes does not necessarily need such a heavy structure for all time frames especially when the cross-talker challenge happens only occasionally. For example, in conversation scenarios, most regions contain only a single active speaker, where the separation task downgrades to a single speaker enhancement problem. It turns…
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室（MERL））；Toyohashi University of Technology（丰桥技术科学大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Recently, there has been growing interest in multi-speaker speech recognition, where the utterances of multiple speakers are recognized from their mixture. Promising techniques have been proposed for this task, but earlier works have required additional training data such as isolated source signals or senone alignments for effective learning. In this paper, we propose a new sequence-to-sequence framework to directly decode multiple label sequences from a single speech sequence by unifying source separation and speech recognition functions in an end-to-end manner. We further propose a new objec…
We present a multi-channel database of overlapping speech for training, evaluation, and detailed analysis of source separation and extraction algorithms: SMS-WSJ -- Spatialized Multi-Speaker Wall Street Journal. It consists of artificially mixed speech taken from the WSJ database, but unlike earlier databases we consider all WSJ0+1 utterances and take care of strictly separating the speaker sets present in the training, validation and test sets. When spatializing the data we ensure a high degree of randomness w.r.t. room size, array center and rotation, as well as speaker position. Furthermore…
