H
Hemant A. Patil
Researcher at Dhirubhai Ambani Institute of Information and Communication Technology
Publications - 245
Citations - 2396
Hemant A. Patil is an academic researcher from Dhirubhai Ambani Institute of Information and Communication Technology. The author has contributed to research in topics: Mel-frequency cepstrum & Speaker recognition. The author has an hindex of 20, co-authored 215 publications receiving 1826 citations. Previous affiliations of Hemant A. Patil include Indian Institute of Chemical Technology & Indian Institute of Technology Kharagpur.
Papers
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Proceedings ArticleDOI
Time-Frequency Masking-Based Speech Enhancement Using Generative Adversarial Network
TL;DR: The proposed system significantly improves over a recent GAN-based speech enhancement system in improving speech quality, while maintaining a better trade-off between less speech distortion and more effective removal of background interferences present in the noisy mixture.
Proceedings ArticleDOI
Combining evidences from mel cepstral, cochlear filter cepstral and instantaneous frequency features for detection of natural vs. spoofed speech.
Tanvina B. Patel,Hemant A. Patil +1 more
TL;DR: A detector based on combination of cochlear filter cepstral coefficients (CFCC) and change in instantaneous frequency (IF), (i.e., CFCCIF) to detect natural vs. spoofed speech for ASVspoof 2015 challenge is proposed.
Proceedings ArticleDOI
Unsupervised Filterbank Learning Using Convolutional Restricted Boltzmann Machine for Environmental Sound Classification.
TL;DR: Using CNN classifier, the ConvRBM filterbank and its score-level fusion with the Mel filterbank energies (FBEs) gave an absolute improvement of 10.65 %, and 18.70 % in the classification accuracy, respectively, over FBEs alone on the ESC-50 database, shows that the proposed ConvR BM filterbank also contains highly complementary information over the Mel filters, which is helpful in the ESC task.
Proceedings ArticleDOI
Novel Variable Length Teager Energy Separation Based Instantaneous Frequency Features for Replay Detection.
TL;DR: A novel replay detector based on Variable length Teager Energy OperatorEnergy Separation Algorithm-Instantaneous Frequency Cosine Coefficients (VESA-IFCC) for the ASV spoof 2017 challenge is proposed and the performance of the proposed feature set is compared with the features developed for detecting synthetic and voice converted speech.
Journal ArticleDOI
Advances in anti-spoofing: from the perspective of ASVspoof challenges
TL;DR: The literature review of ASV spoof detection, novel acoustic feature representations, deep learning, end-to-end systems, etc, along with recent efforts to develop countermeasures for spoof speech detection (SSD) task are presented.