Workshop on Applications of Signal Processing to Audio and Acoustics 

About: Workshop on Applications of Signal Processing to Audio and Acoustics is an academic conference. The conference publishes majorly in the area(s): Audio signal processing & Microphone. Over the lifetime, 1066 publications have been published by the conference receiving 22043 citations.

The CIPIC HRTF database

Abstract: This paper describes a public-domain database of high-spatial-resolution head-related transfer functions measured at the UC Davis CIPIC Interface Laboratory and the methods used to collect the data.. Release 1.0 (see http://interface.cipic.ucdavis.edu) includes head-related impulse responses for 45 subjects at 25 different azimuths and 50 different elevations (1250 directions) at approximately 5/spl deg/ angular increments. In addition, the database contains anthropometric measurements for each subject. Statistics of anthropometric parameters and correlations between anthropometry and some temporal and spectral features of the HRTFs are reported.

Non-negative matrix factorization for polyphonic music transcription

Abstract: We present a methodology for analyzing polyphonic musical passages comprised of notes that exhibit a harmonically fixed spectral profile (such as piano notes). Taking advantage of this unique note structure, we can model the audio content of the musical passage by a linear basis transform and use non-negative matrix decomposition methods to estimate the spectral profile and the temporal information of every note. This approach results in a very simple and compact system that is not knowledge-based, but rather learns notes by observation.

The reverb challenge: Acommon evaluation framework for dereverberation and recognition of reverberant speech

Abstract: Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we propose a common evaluation framework including datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques. The proposed framework will be used as a common basis for the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. This paper describes the rationale behind the challenge, and provides a detailed description of the evaluation framework and benchmark results.

To catch a chorus: using chroma-based representations for audio thumbnailing

Abstract: An important application for use with multimedia databases is a browsing aid, which allows a user to quickly and efficiently preview selections from either a database or from the results of a database query. Methods for facilitating browsing, though, are necessarily media dependent. We present one such method that produces short, representative samples (or "audio thumbnails") of selections of popular music. This method attempts to identify the chorus or refrain of a song by identifying repeated sections of the audio waveform. A reduced spectral representation of the selection based on a chroma transformation of the spectrum is used to find repeating patterns. This representation encodes harmonic relationships in a signal and thus is ideal for popular music, which is often characterized by prominent harmonic progressions. The method is evaluated over a sizable database of popular music and found to perform well, with most of the errors resulting from songs that do not meet our structural assumptions.

Stable and fast update rules for independent vector analysis based on auxiliary function technique

Abstract: This paper presents stable and fast update rules for independent vector analysis (IVA) based on auxiliary function technique. The algorithm consists of two alternative updates: 1) weighted covariance matrix updates and 2) demixing matrix updates, which include no tuning parameters such as step size. The monotonic decrease of the objective function at each update is guaranteed. The experimental evaluation shows that the derived update rules yield faster convergence and better results than natural gradient updates.