SRA: A Web-based Research Tool for Spectral and Roughness Analysis of Sound Signals (Version 2.0)
Abstract
SRA is a web-based tool that performs Spectral and Roughness Analysis on user-submitted sound files (.wav and .aif formats). Spectral analysis incorporates an improved Short-Time Fourier Transform (STFT) algorithm [Fulop, S.A. and Fitz, K. (2006). “Algorithms for computing the time corrected instantaneous frequency (reassigned) spectrogram, with applications,” J. Acoust. Soc. Am. 119(1): 360-371.] and automates spectral peak-picking using Loris open source C++ class library components [Fulop, S.A. and Fitz, K. (2007). “Separation of components from impulses in reassigned spectrograms,” J. Acoust. Soc. Am. 121(3): 1510-1518.]. Users can set three spectral analysis/peak-picking parameters: analysis bandwidth, spectral-amplitude normalization, and spectral amplitude threshold. These are described in detail within the tool, including suggestions on settings appropriate to the submitted files and research questions of interest. The spectral values obtained from the analysis enter a roughness calculation model [Vassilakis, P.N. (2005). "Auditory roughness as a means of musical expression," Selected Reports in Ethnomusicology 12: 119-144.], outputting roughness values at user specified points within a file or roughness profiles at user specified time intervals. The tool offers research background on spectral analysis, auditory roughness, and the algorithms used, including links to relevant publications. Spectral and roughness analysis of sound signals finds applications in music cognition, musical analysis, speech processing, and music teaching research, as well as in medicine and other areas.
[Supported by a Northwest Academic Computing Consortium grant to J. Middleton, Eastern Washington University.]
Suggested Citation
Vassilakis, P.N. and Fitz, K. (2008). "SRA: A Web-based Research Tool for Spectral and Roughness Analysis of Sound Signals." Supported by a Northwest Academic Computing Consortium grant to J. Middleton, Eastern Washington University. [http://musicalgorithms.ewu.edu/algorithms/roughness.html]