Proceedings of the international conference on Multimedia
Efficient advertisement discovery for audio podcast content using candidate segmentation
EURASIP Journal on Audio, Speech, and Music Processing
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Automatic detection of predefined events in speech and audio signals is a challenging and promising subject in signal processing. One important application of such detection is removal or suppression of unwanted sounds in audio recordings, for instance in the professional music industry, where the demand for quality is very high. Breath sounds, which are present in most song recordings and often degrade the aesthetic quality of the voice, are an example of such unwanted sounds. Another example is bad pronunciation of certain phonemes. In this paper, we present an automatic algorithm for accurate detection of breaths in speech or song signals. The algorithm is based on a template matching approach, and consists of three phases. In the first phase, a template is constructed from mel frequency cepstral coefficients (MFCCs) matrices of several breath examples and their singular value decompositions, to capture the characteristics of a typical breath event. Next, in the initial processing phase, each short-time frame is compared to the breath template, and marked as breathy or nonbreathy according to predefined thresholds. Finally, an edge detection algorithm, based on various time-domain and frequency-domain parameters, is applied to demarcate the exact boundaries of each breath event and to eliminate possible false detections. Evaluation of the algorithm on a database of speech and songs containing several hundred breath sounds yielded a correct identification rate of 98% with a specificity of 96%