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 existing work use empirical analysis to show this property. In this paper, we show mathematically that for a single resonator, the ratio of the value of the peak in the magnitude spectrum to the value at a frequency that is n dB below the peak, is always much lower than the ratio of that of the minimum phase group delay spectrum. The results are extended for multiple resonators using numerical analyses. The theoretical results are reinforced using three applications, namely, pitch estimation, formant estimation and onset detection. The average deviation from the location of the pitch value/formant value/musical onset is about 53% lower than that of similar techniques that use the magnitude spectrum of the signal. © 2016 Elsevier B.V.

Hema Murthy

Department of Computer Science and Engineering

Jilt Sebastian

Manoj Kumar P. A

ACOUSTIC VARIABLES MEASUREMENT

Cellular neural networks

Group delay

Resonators

SPECTRAL RESOLUTION

Spectrum Analysis

empirical analysis

FORMANT ESTIMATION

GROUP DELAY FUNCTIONS

GROUP DELAY SPECTRUMS

HIGH SPECTRAL RESOLUTION

MULTIPLE RESONATORS

Onset detection

PITCH ESTIMATION

Audio signal processing

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Speech Communication

Spike estimation from calcium (Ca2+) fluorescence signals is a fundamental and challenging problem in neuroscience. Several models and algorithms have been proposed for this task over the past decade. Nevertheless, it is still hard to achieve accurate spike positions from the Ca2+ fluorescence signals. While existing methods rely on data-driven methods and the physiology of neurons for modeling the spiking process, this paper exploits the nature of the fluorescence responses to spikes using signal processing. We first motivate the problem by a novel analysis of the high-resolution property of minimum-phase group delay (GD) functions for multi-pole resonators. The resonators could be connected either in series or in parallel. The Ca2+ indicator responds to a spike with a sudden rise, that is followed by an exponential decay. We interpret the Ca2+ signal as the response of an impulse train to the change in Ca2+ concentration, where the Ca2+ response corresponds to a resonator. We perform minimum-phase GD-based filtering of the Ca2+ signal for resolving spike locations. The performance of the proposed algorithm is evaluated on nine datasets spanning various indicators, sampling rates, and mouse brain regions. The proposed approach, GDspike, is compared with other spike estimation methods, including MLspike, Vogelstein de-convolution algorithm, and data-driven spike-triggered mixture model. The performance of GDspike is superior to that of the Vogelstein algorithm and is comparable to that of MLspike. It can also be used to post-process the output of MLspike, which further enhances the performance. © 1991-2012 IEEE.

Fulltext

IEEE Transactions on Signal Processing

Spike estimation from fluorescence signals using high-resolution property of group delay

An analysis of the high resolution property of group delay function with applications to audio signal processing

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 use of modified group delay (MOD-GD) function for learning the time-frequency masks of the sources in the monaural BSS problem. We demonstrate the use of MOD-GD through two music source separation tasks: singing voice separation on the MIR-1K data set and vocal-violin separation on the Carnatic music data set. We find that it outperforms the state-of-the-art feature in terms of Signal to Interference Ratio (SIR). Moreover, training and testing times are significantly reduced (by 50%) without compromising on the performance for the best performing DRNN configuration. © 2016 IEEE.

2016 International Conference on Signal Processing and Communications, SPCOM 2016

Group delay based music source separation using deep recurrent neural networks

In this paper, we propose a modified group delay based method for melodic pitch extraction from heterophonic music. The power spectrum of the music signal is first flattened in order that the system characteristics are annihilated, while the characteristics of the source are emphasized. The modified group delay function of this signal produces peaks at multiples of the pitch period. The first 3 peaks are used to determine the actual pitch period. The performance of the proposed system was evaluated on two datasets ADC-2004, and LabROSA. The performance is comparable to that of other magnitude spectrum based approaches. The algorithms are also applied to heterophonic music, namely Carnatic Music. As ground truth is not available for Carnatic Music, the pitch contours were used to synthesize the music, which was evaluated for correctness by a professional musician. © 2013 IEEE.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Group delay based melody monopitch extraction from music

Traditionally, the information in speech signals is represented in terms of features derived from short-time Fourier analysis. In this analysis the features extracted from the magnitude of the Fourier transform (FT) are considered, ignoring the phase component. Although the significance of the FT phase was highlighted in several studies over the recent three decades, the features of the FT phase were not exploited fully due to difficulty in computing the phase and also in processing the phase function. The information in the short-time FT phase function can be extracted by processing the derivative of the FT phase, i. e., the group delay function. In this paper, the properties of the group delay functions are reviewed, highlighting the importance of the FT phase for representing information in the speech signal. Methods to process the group delay function are discussed to capture the characteristics of the vocal-tract system in the form of formants or through a modified group delay function. Applications of group delay functions for speech processing are discussed in some detail. They include segmentation of speech into syllable boundaries, exploiting the additive and high resolution properties of the group delay functions. The effectiveness of segmentation of speech, and the features derived from the modified group delay are demonstrated in applications such as language identification, speech recognition and speaker recognition. The paper thus demonstrates the need to exploit the potential of the group delay functions for development of speech systems. © 2011 Indian Academy of Sciences.

Sadhana - Academy Proceedings in Engineering Sciences

Group delay functions and its applications in speech technology

This paper demonstrates the robustness of group delay based features to additive noise. First, we analytically show the robustness of group delay based representations. The analysis makes use of the fact that, for minimum-phase signals, the group delay function can be represented in terms of the cepstral coefficients of the log-magnitude spectrum. Such a representation results in the speech spectrum dominating over the noise spectrum, both at low and high SNRs. Further, we experimentally demonstrate the robustness of the representation on a voice activity detection (VAD) task, comparing a group delay based VAD algorithm with standard VAD methods as well as a magnitude-spectrum based method. © 2011 Springer Science+Business Media, LLC.

International Journal of Speech Technology

Robustness of group delay representations for noisy speech signals

IEEE Transactions on Audio, Speech and Language Processing

On the importance of phase in human speech recognition

Journal	Data powered by TypesetSpeech Communication
Publisher	Data powered by TypesetElsevier B.V.
ISSN	01676393
Open Access	No