Eurasip Journal on Audio Speech and Music Processing最新文献

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Biomimetic spectro-temporal features for music instrument recognition in isolated notes and solo phrases. 在孤立音符和独奏乐句中识别乐器的仿生光谱-时间特征。

IF 2.4 3区计算机科学 Q2 Physics and Astronomy

Eurasip Journal on Audio Speech and Music Processing

Pub Date : 2015-01-01 DOI: 10.1186/s13636-015-0070-9

Kailash Patil, Mounya Elhilali

The identity of musical instruments is reflected in the acoustic attributes of musical notes played with them. Recently, it has been argued that these characteristics of musical identity (or timbre) can be best captured through an analysis that encompasses both time and frequency domains; with a focus on the modulations or changes in the signal in the spectrotemporal space. This representation mimics the spectrotemporal receptive field (STRF) analysis believed to underlie processing in the central mammalian auditory system, particularly at the level of primary auditory cortex. How well does this STRF representation capture timbral identity of musical instruments in continuous solo recordings remains unclear. The current work investigates the applicability of the STRF feature space for instrument recognition in solo musical phrases and explores best approaches to leveraging knowledge from isolated musical notes for instrument recognition in solo recordings. The study presents an approach for parsing solo performances into their individual note constituents and adapting back-end classifiers using support vector machines to achieve a generalization of instrument recognition to off-the-shelf, commercially available solo music.

乐器的身份体现在用乐器演奏的音符的声学属性上。最近，有人认为，音乐身份(或音色)的这些特征可以通过包括时域和频域的分析来最好地捕捉;聚焦于信号在光谱时间空间中的调制或变化。这种表征模仿了谱颞感受野(STRF)分析，该分析被认为是哺乳动物中枢听觉系统处理的基础，特别是在初级听觉皮层的水平。在连续的独奏录音中，这种STRF表示如何很好地捕捉乐器的音色身份仍然不清楚。目前的工作调查了STRF特征空间在独奏乐句中乐器识别的适用性，并探索了在独奏录音中利用孤立音符知识进行乐器识别的最佳方法。该研究提出了一种将独奏表演解析为单个音符成分的方法，并使用支持向量机调整后端分类器，以实现对现成的、市售的独奏音乐的乐器识别的泛化。

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引用次数: 18

Biomimetic multi-resolution analysis for robust speaker recognition. 鲁棒说话人识别的仿生多分辨率分析。

IF 2.4 3区计算机科学 Q2 Physics and Astronomy

Eurasip Journal on Audio Speech and Music Processing

Pub Date : 2012-01-01 Epub Date: 2012-09-07 DOI: 10.1186/1687-4722-2012-22

Sridhar Krishna Nemala, Dmitry N Zotkin, Ramani Duraiswami, Mounya Elhilali

Humans exhibit a remarkable ability to reliably classify sound sources in the environment even in presence of high levels of noise. In contrast, most engineering systems suffer a drastic drop in performance when speech signals are corrupted with channel or background distortions. Our brains are equipped with elaborate machinery for speech analysis and feature extraction, which hold great lessons for improving the performance of automatic speech processing systems under adverse conditions. The work presented here explores a biologically-motivated multi-resolution speaker information representation obtained by performing an intricate yet computationally-efficient analysis of the information-rich spectro-temporal attributes of the speech signal. We evaluate the proposed features in a speaker verification task performed on NIST SRE 2010 data. The biomimetic approach yields significant robustness in presence of non-stationary noise and reverberation, offering a new framework for deriving reliable features for speaker recognition and speech processing.

人类表现出一种非凡的能力，即使在噪音很大的环境中也能可靠地对声源进行分类。相比之下，当语音信号被信道或背景失真破坏时，大多数工程系统的性能会急剧下降。我们的大脑配备了复杂的语音分析和特征提取机制，这对于提高语音自动处理系统在不利条件下的性能具有重要的借鉴意义。本文介绍的工作探索了一种生物驱动的多分辨率说话人信息表示，该信息表示是通过对语音信号的信息丰富的光谱时间属性进行复杂但计算效率高的分析获得的。我们在NIST SRE 2010数据上执行的说话人验证任务中评估了所提出的特征。仿生方法在存在非平稳噪声和混响时具有显著的鲁棒性，为获得可靠的说话人识别和语音处理特征提供了新的框架。

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引用次数: 5

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