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A survey of sound source localization with deep learning methods

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2022)

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Summary: Understanding and controlling latent representations in deep generative models is a challenging yet important problem. In this work, the source-filter model of speech production naturally arises as orthogonal subspaces of the VAE latent space. A method is proposed to identify and control the source-filter speech factors within the latent subspaces, as well as a robust f(0) estimation method.

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Deep learning based multi-source localization with source splitting and its effectiveness in multi-talker speech recognition

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Summary: This paper proposes a novel supervised learning method using deep neural networks for multi-source localization in multi-talker conversation analysis. The method utilizes a source splitting mechanism to estimate the direction of arrival (DOA) of all speakers simultaneously from the audio mixture. The proposed method outperforms existing deep learning methods by performing utterance level prediction and incorporating temporal selection and averaging inside the network. Experimental results demonstrate the effectiveness of a variant of earth mover distance (EMD) in classifying DOA at a high resolution. Moreover, the estimated DOAs are used as additional input features in a speech recognition baseline, significantly improving the recognition performance.

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Summary: This paper provides a survey of Multi-Task Learning (MTL) from the perspective of algorithmic modeling, applications, and theoretical analyses. It discusses different MTL algorithms and their characteristics, as well as the combination of MTL with other learning paradigms. The paper also reviews MTL models for large-scale tasks or high-dimensional data, as well as dimensionality reduction and feature hashing. Real-world applications of MTL are examined, and theoretical analyses and future directions are discussed.

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Sound source localization based on multi-task learning and image translation network

Yifan Wu et al.

Summary: Supervised learning-based sound source localization methods have been successful in achieving high accuracy, while a new method called MTIT using Multi-Task learning and Image Translation network for SSL is introduced in this paper. By extracting spatial features and utilizing multi-task learning, MTIT outperforms baseline methods in dynamic environments and shows good generalization performance.

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Soo Young Lee et al.

Summary: A deep learning approach is proposed for multiple sound source localization with high resolution and accuracy, regardless of the sound sources' positions on the grid points. By introducing a target function to obtain spatial source distribution maps, the proposed model can accurately predict the positions and strengths of multiple sound sources, outperforming model-based methods. The model is evaluated on a dataset with monopole sources on a square plane with a spiral array of microphones, demonstrating precise localization results irrespective of frequency and the number of sound sources.

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Juan Manuel Vera-Diaz et al.

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Hodaya Hammer et al.

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BeamLearning: An end-to-end deep learning approach for the angular localization of sound sources using raw multichannel acoustic pressure dataa)

Hadrien Pujol et al.

Summary: This paper introduces a multiresolution deep learning approach called BeamLearning for sound source localization, which aims to capture relevant information from unprocessed acoustic signals and outperforms traditional methods in terms of accuracy and efficiency in noisy environments.

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Bing Yang et al.

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Summary: Supervised learning methods are effective for DOA estimation, and in this study, a CNN approach with LSTM extension showed superior performance. By adjusting the training data generation framework to incorporate temporal context, a gradual evolution of source activity was achieved. Experimental results demonstrated the effectiveness of using LSTM extension for speaker localization tasks.

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Elias Zea et al.

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Neural Network Adaptation and Data Augmentation for Multi-Speaker Direction-of-Arrival Estimation

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Relative transfer function identification using speech seals

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