1. Audio Processing

mfcc.m   Computes Mel Frequency Cepstral Coefficients (from Toolbox by Malcolm Slaney)

extractMFCC.m   Extracts all MFCC vectors for a given audio file (not yet commented)

EXTRACTMFCC: Extract Mel Frequency Cepstral Coefficients from a file   or an audio vector. This function extracts MFCCs using mfcc.m by   reading frames of audio data from the file or audio vector.     Several constants are declared and can be changed:       windowSize = 256 (audio samples per frame resulting in one MFCC       feature vector)       rangeRead = 4000 * windowSize (amount of audio samples read from       file at one time)   CEPS = EXTRACTMFCC(audiofile) for a wave audio file   CEPS = EXTRACTMFCC(audioVector, samplingFrequency) for an audio vector   If samplingFrequency is not passed, a warning is printed and a default   frequency of 48kHz is used.

speakerchange.m   Applies BIC to determine speaker changes in an audio file. Requires audio file (wave) as input. Depends on mfcc.m. Returns table of segmented speakers.

SPEAKERCHANGE: Compute speaker changes in an audio file based on BIC   T = SPEAKERCHANGE(audiofile) for a wave audio file returns a table of   speaker changes where rows are segmented speakers and columns are:       1: begin of segment in wave samples       2: end of segment in wave samples       3: begin of segment in MFCC CEPS index       4: begin of segment in MFCC CEPS index   [T, C] = SPEAKERCHANGE(audiofile) returns segment table T, as well as   extracted cepstral coefficients in matrix C with rows(C) as MFCC   coefficient dimension and columns(C) as feature vectors over time.   [T, C, B] = SPEAKERCHANGE(audiofile) returns segment table T, cepstral   coefficients C, as well as vector B representing the BIC values over   the entire audio vector.   Implementation: Alexander Haubold       3/17/2006:  Added 2nd level (fine) BIC evaluation on top of                     first level (coarse) BIC evaluation. Precision of                     speaker change increases significantly   Paper detailing BIC: S.S. Chen, P.S. Gopalakrishnan. Speaker,     environment and channel detection and clustering via the Bayesian     information criterion. Proceedings of the DARPA Broadcast News     Transcription and Understanding Workshop, Landsdowne, VA, 1998,     pp. 127-132.