Scaling behaviour of frequency-wise amplitude variation of speech and music signal, over time, has been analysed using non-stationary, complex-network based method and found that it varies a lot from frequency to frequency for music whereas it’s almost consistent for speech signal. In this experiment drone signal is taken as the basic music signal and it has been established that music and speech are so different with respect to both of frequency and amplitude content that our left auditory cortical areas have adapted to be neurocognitively specialized in speech perception and right ones in music. Further a non-invasive application for detecting neurological illness like Autism Spectrum Disorder has been proposed, using the different patterns of variation of scaling behavior of every aspect(amplitude, frequency and time-displacement profile) for speech and music.

Speech and music signals are multifractal phenomena. The time-displacement profile of speech and music signal show strikingly different scaling behavior. This complex-network based approach [Visibility Graph] used to study the scaling behavior of frequency-wise amplitude variation of speech and music signal shows that the scaling behavior of amplitude-profile of music varies a lot from frequency to frequency whereas it’s almost consistent for speech signal. Our left auditory cortical areas are proposed to be neurocognitively specialized in speech perception and right ones in music. Hence we can conclude that human brain might have been adapted to the distinctly different scaling behavior of speech and music signals and developed different decoding mechanisms, as-if following the so-called Fractal-Darwinism. Using this method, we can capture every non-stationary aspects of the acoustic properties of the source signal, to the deepest level, which have huge neurocognitive significance. Further, a model is proposed for non-invasive application for detecting neurological illness, using the different patterns of variation of scaling behavior for speech and music.

(a) Amplitude variation over time for speech signal for a particular frequency, (b) Amplitude variation over time for drone signal for a particular frequency, (c) Trend of  values for first 20 dominant frequencies for a speech audio sample, (d) Trend of  values for first 20 dominant frequencies for a speech audio sample, (d) Trend of  values for first


Authors: Susmita Bhaduri and Dipak Ghosh
Archives of Acoustics (Probable Impact Factor: 1.2)
Status :Published
Area : Music Signal analysis