  Distinct striation patterns are observed in the spectrograms of speech and
music. This motivated us to propose three novel time-frequency features for
speech-music classification. These features are extracted in two stages. First,
a preset number of prominent spectral peak locations are identified from the
spectra of each frame. These important peak locations obtained from each frame
are used to form Spectral peak sequences (SPS) for an audio interval. In second
stage, these SPS are treated as time series data of frequency locations. The
proposed features are extracted as periodicity, average frequency and
statistical attributes of these spectral peak sequences. Speech-music
categorization is performed by learning binary classifiers on these features.
We have experimented with Gaussian mixture models, support vector machine and
random forest classifiers. Our proposal is validated on four datasets and
benchmarked against three baseline approaches. Experimental results establish
the validity of our proposal.
