Utterance Clustering Using Stereo Audio Channels
Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- and right-channel audio signals in a few different ways and then extracted embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter sharing Gaussian mixture model was conducted to train the model for each speaker. In the testing phase, the speaker with the maximum likelihood was selected as the detected speaker. Results of experiments with real audio recordings of multi-person discussion sessions showed that the proposed method that used multichannel audio signals achieved significantly better performance than a conventional method with mono audio signals in more complicated conditions.
Code (0)
등록된 구현이 없습니다.
Tasks
Audio Signal ProcessingClusteringSpeaker DiarizationSimilar Papers 제목 키워드 기반
Stereo InSE-NET: Stereo Audio Quality Predictor Transfer Learned from Mono InSE-NET
Automatic coded audio quality predictors are typically designed for evaluating single channels without considering any spatial aspects. With InSE-NET [1], we demonstrated mimicking a state-of-the-art coded audio quality …
Using perceptive subbands analysis to perform audio scenes cartography
Audio scene cartography for real or simulated stereo recordings is presented. This audio scene analysis is performed doing successively: a perceptive 10-subbands analysis, calculation of temporal laws for relative delays…
Audio-Guided Dynamic Modality Fusion with Stereo-Aware Attention for Audio-Visual Navigation
In audio-visual navigation (AVN) tasks, an embodied agent must autonomously localize a sound source in unknown and complex 3D environments based on audio-visual signals. Existing methods often rely on static modality fus…
Reinforcement LearningVisual NavigationBootstrapping single-channel source separation via unsupervised spatial clustering on stereo mixtures
Separating an audio scene into isolated sources is a fundamental problem in computer audition, analogous to image segmentation in visual scene analysis. Source separation systems based on deep learning are currently the …
ClusteringImage SegmentationSemantic SegmentationUnsupervised Spatial ClusteringWhat Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channels
Prosody -- the melody of speech -- conveys critical information often not captured by the words or text of a message. In this paper, we propose an information-theoretic approach to quantify how much information is expres…