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Accurate estimation of spike train from calcium (Ca2+) fluorescence signals is challenging owing to significant fluctuations of fluorescence level. This paper proposes a non-model-based approach for spike train inference using group delay (GD) analysis. It primarily exploits the property that change in Ca2+ fluorescence corresponding to a spike has a notable onset location followed by a decaying transient...
This paper provides a new insight into the high resolution property of the negative derivative of the phase response of a system. Group delay functions have been proposed and applied successfully as an alternative to conventional magnitude spectrum based applications in speech and music processing. One of the reasons claimed for its superior performance is the high spectral resolution. Most of the...
Deep Recurrent Neural Networks (DRNNs) have been most successfully used in solving the challenging task of separating sources from a single channel acoustic mixture. Conventionally, magnitude spectra are being used to learn the characteristics of individual sources in such monaural blind source separation (BSS) task. The phase spectra which inherently contain the timing information is often ignored...
In this work, we explore the task of musical onset detection in Carnatic music by choosing five major percussion instruments: the mridangam, ghatam, kanjira, morsing and thavil. We explore the musical characteristics of the strokes for each of the above instruments, motivating the challenge in designing an onset detection algorithm. We propose a non-model based algorithm using the minimum-phase group...
This work presents an approach for pitch extraction based on Grating Compression Transform (GCT) on harmonically-enhanced Modified Group-Delay-gram (Modgdgram). The work explores the use of peakedness and high resolution properties of the group delay functions and the ability of GCT to smear harmonically related components in the spectrum and to track pitch across frames. The power spectrum of the...
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