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

Sebastian, Jilt; Kumar, Pratyush; Murthy, Hema A.

doi:10.1016/j.specom.2015.12.008

Cited by 16 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…are represented using Hilbert envelope of the differenced NGD (HNGD) function, which has a good resolution around the formants [24,27]. HNGD spectra is computed at each sample leading to a high temporal resolution.…”

Section: Ztw Methods and Dominant Resonance Based Representation The Smentioning

confidence: 99%

Identification and Classification of Fricatives in Speech Using Zero Time Windowing Method

Prasad

Yegnanarayana

2018

Interspeech 2018

View full text Add to dashboard Cite

Fricatives are produced by creating a turbulence in the airflow by passing it through a stricture in the vocal tract cavity. Fricatives are characterized by their noise-like behavior, which makes it difficult to analyze. Difference in the place of articulation leads to different classes of fricatives. Identification of fricative segment boundaries in speech helps in improving the performance of several applications. The present study attempts towards the identification and classification of fricative segments in continuous speech, based on the statistical behavior of instantaneous spectral characteristics. The proposed method uses parameters such as the dominant resonance frequencies, the center of gravity along with the statistical moments of the spectrum obtained using the zero time windowing (ZTW) method. The ZTW spectra exhibits a high temporal resolution and therefore gives accurate segment boundaries in speech. The proposed algorithm is tested on the TIMIT dataset for English language. A high identification rate of 97.5% is achieved for segment boundaries of the sibilant fricative class. Voiced nonsibilants show a lower identification rate than their voiceless counterparts due to their vowel-like spectral characteristics. A high classification rate of 93.2% is achieved between sibilants and nonsibilants.

show abstract

Section: Ztw Methods and Dominant Resonance Based Representation The Smentioning

confidence: 99%

Identification and Classification of Fricatives in Speech Using Zero Time Windowing Method

Prasad

Yegnanarayana

2018

Interspeech 2018

View full text Add to dashboard Cite

show abstract

“…Thus, in order to robustly extract discriminative information from pertinent spectral regions, the time-frequency representation of the speech signal should provide high spectral resolution. Group delay functions have been shown to have higher spectral resolution in comparison with the magnitude spectrum [12]. Moreover, GD-gram contains both power and phase spectrum information [11,13], thus making it a good feature representation for end-to-end learning for spoof detection.…”

Section: Group Delay Gram (Gd-gram)mentioning

confidence: 99%

End-To-End Audio Replay Attack Detection Using Deep Convolutional Networks with Attention

Tom¹,

Jain

Dey

2018

Interspeech 2018

View full text Add to dashboard Cite

With automatic speaker verification (ASV) systems becoming increasingly popular, the development of robust countermeasures against spoofing is needed. Replay attacks pose a significant threat to the reliability of ASV systems because of the relative difficulty in detecting replayed speech and the ease with which such attacks can be mounted. In this paper, we propose an end-to-end deep learning framework for audio replay attack detection. Our proposed approach uses a novel visual attention mechanism on time-frequency representations of utterances based on group delay features, via deep residual learning (an adaptation of ResNet-18 architecture). Using a single model system, we achieve a perfect Equal Error Rate (EER) of 0% on both the development as well as the evaluation set of the ASVspoof 2017 dataset, against a previous best of 0.12% on the development set and 2.76% on the evaluation set reported in the literature. This highlights the efficacy of our feature representation and attention-based architecture in tackling the challenging task of audio replay attack detection.

show abstract

“…This algorithm has been used for segmenting human speech into syllable-like units [10]. The high-resolution property of the group-delay helps in resolving the closely placed poles in a signal [12]. The group delay segmentation algorithm is as follows.…”

Section: Unsupervised Segmentationmentioning

confidence: 99%

Discovering Language in Marmoset Vocalization

Verma¹,

Prateek²,

Pandia³

et al. 2017

Interspeech 2017

Self Cite

View full text Add to dashboard Cite

Various studies suggest that marmosets (Callithrix jacchus) show behavior similar to that of humans in many aspects. Analyzing their calls would not only enable us to better understand these species but would also give insights into the evolution of human languages and vocal tract. This paper describes a technique to discover the patterns in marmoset vocalization in an unsupervised fashion. The proposed unsupervised clustering approach operates in two stages. Initially, voice activity detection (VAD) is applied to remove silences and non-voiced regions from the audio. This is followed by a group-delay based segmentation on the voiced regions to obtain smaller segments. In the second stage, a two-tier clustering is performed on the segments obtained. Individual hidden Markov models (HMMs) are built for each of the segments using a multiple frame size and multiple frame rate. The HMMs are then clustered until each cluster is made up of a large number of segments. Once all the clusters get enough number of segments, one Gaussian mixture model (GMM) is built for each of the clusters. These clusters are then merged using Kullback-Leibler (KL) divergence. The algorithm converges to the total number of distinct sounds in the audio, as evidenced by listening tests.

show abstract

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

Cited by 16 publications

References 18 publications

Identification and Classification of Fricatives in Speech Using Zero Time Windowing Method

Identification and Classification of Fricatives in Speech Using Zero Time Windowing Method

End-To-End Audio Replay Attack Detection Using Deep Convolutional Networks with Attention

Discovering Language in Marmoset Vocalization

Contact Info

Product

Resources

About