Sparse representations for audio

音频的稀疏表示

• 批准号: 194542-2010
• 负责人: Lefebvre, Roch
• 金额: $ 2.4万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637468
• 关键词: Sparse; representations; audio

Over the past decades, advances in digital audio processing have allowed numerous applications to emerge, mature and in many cases become commodities in everyday life. Cellphones, smartphones, MP3 players, Voice over IP (VoIP) software and even Youtube all have in common advanced audio processing in their core. Specifically, speech and/or audio compression algorithms allow using transmission bandwidth and storage space sparingly while providing users the audio quality they expect for a given application. The need for performant audio processing algorithms, and in particular compression algorithms, can be expected to grow with demanding applications such as digital multichannel radio broadcast for mobile users (in cars, for example) and wireless multimedia of all sorts to be used on mobile devices.The purpose of this research project is to study new approaches for analysing, decomposing and representing audio signals in order to obtain sparse and very sparse representations suitable for compression, but also for signal enhancement, sound source separation and signal classification. Instead of attempting to improve on the classical approaches based on block-by-block analysis using transforms and prediction filters, the focus will be on adaptive signal segmentation, perceptually derived basis functions and audio object representation. The resulting algorithms will thus produce audio signal approximations which will range from synthetic sound objects (as in MIDI, for example) to high-fidelity source reproduction, rather than from high distortion to low distortion. To achieve the next significant gains in sparse audio representations, waveform tracking constraints have to be relaxed, and ultimately completely replaced by excitation pattern similarities in the auditory system. This research project aims to contribute knowledge and technological advances towards that goal.

在过去的几十年里，数字音频处理的进步使许多应用程序出现，成熟，并在许多情况下成为日常生活中的商品。手机、智能手机、MP3播放器、IP语音（VoIP）软件甚至Youtube的核心都有一个共同的高级音频处理功能。具体来说，语音和/或音频压缩算法允许节省传输带宽和存储空间，同时为用户提供他们期望的给定应用程序的音频质量。对高性能音频处理算法，特别是压缩算法的需求，可以预期会随着诸如移动用户（例如在汽车中）的数字多通道无线电广播和在移动设备上使用的各种无线多媒体等要求苛刻的应用而增长。本研究项目的目的是研究分析、分解和表示音频信号的新方法，以获得适合压缩的稀疏和非常稀疏的表示，也适用于信号增强、声源分离和信号分类。而不是试图改进经典方法基于块逐块分析使用变换和预测滤波器，重点将是自适应信号分割，感知衍生基函数和音频对象表示。由此产生的算法将产生音频信号近似值，其范围从合成声音对象（例如在MIDI中）到高保真源再现，而不是从高失真到低失真。为了在稀疏音频表示中实现下一个显著的增益，波形跟踪约束必须放松，并最终完全被听觉系统中的激励模式相似性所取代。该研究项目旨在为实现这一目标贡献知识和技术进步。