Histogram Equalization-Based Features for Speech, Music, and Song Discrimination

Gallardo-Antolín, Ascensión; Montero, Juan Manuel

doi:10.1109/lsp.2010.2049877

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…On the other hand, segment-based features are computed over longer periods of time (0.5 to 5 s). For example, in [6] segment features are extracted by fitting frame-based features to a reference model using a histogram equalisation transformation. The variation of the spectrum flux and the variation of the zero crossing rate are proposed in [7] as segment-based features.…”

Section: Audio Segmentation Approachesmentioning

confidence: 99%

Multiclass audio segmentation based on recurrent neural networks for broadcast domain data

Gimeno

Viñals

Giménez

et al. 2020

J AUDIO SPEECH MUSIC PROC.

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This paper presents a new approach based on recurrent neural networks (RNN) to the multiclass audio segmentation task whose goal is to classify an audio signal as speech, music, noise or a combination of these. The proposed system is based on the use of bidirectional long short-term Memory (BLSTM) networks to model temporal dependencies in the signal. The RNN is complemented by a resegmentation module, gaining long term stability by means of the tied state concept in hidden Markov models. We explore different neural architectures introducing temporal pooling layers to reduce the neural network output sampling rate. Our findings show that removing redundant temporal information is beneficial for the segmentation system showing a relative improvement close to 5%. Furthermore, this solution does not increase the number of parameters of the model and reduces the number of operations per second, allowing our system to achieve a real-time factor below 0.04 if running on CPU and below 0.03 if running on GPU. This new architecture combined with a data-agnostic data augmentation technique called mixup allows our system to achieve competitive results in both the Albayzín 2010 and 2012 evaluation datasets, presenting a relative improvement of 19.72% and 5.35% compared to the best results found in the literature for these databases.

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Section: Audio Segmentation Approachesmentioning

confidence: 99%

Multiclass audio segmentation based on recurrent neural networks for broadcast domain data

Gimeno

Viñals

Giménez

et al. 2020

J AUDIO SPEECH MUSIC PROC.

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show abstract

“…However, some classes are better described by the statistics computed over longer periods of time (from 0.5 to 5 s long). These characteristics are referred in the literature as segment-based features [29,30]. For example, in [31], a content-based speech discrimination algorithm is designed to exploit the long-term information inherent in the modulation spectrum; and in [32], authors propose two segment-based features: the variance of the spectrum flux (VSF) and the variance of the zero crossing rate (VZCR).…”

Section: General Description Of Audio Segmentation Systemsmentioning

confidence: 99%

Albayzín-2014 evaluation: audio segmentation and classification in broadcast news domains

Castán

Tavarez

López-Otero

et al. 2015

J AUDIO SPEECH MUSIC PROC.

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Audio segmentation is important as a pre-processing task to improve the performance of many speech technology tasks and, therefore, it has an undoubted research interest. This paper describes the database, the metric, the systems and the results for the Albayzín-2014 audio segmentation campaign. In contrast to previous evaluations where the task was the segmentation of non-overlapping classes, Albayzín-2014 evaluation proposes the delimitation of the presence of speech, music and/or noise that can be found simultaneously. The database used in the evaluation was created by fusing different media and noises in order to increase the difficulty of the task. Seven segmentation systems from four different research groups were evaluated and combined. Their experimental results were analyzed and compared with the aim of providing a benchmark and showing up the promising directions in this field.

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“…We categorize the 8 feature descriptors based on their types into a few subgroups. A. Histogram Based Descriptors: A very standard approach that is adopted for computing signal descriptors is to adopt a histogram-based representation [10]. The following 3 descriptors draw inspiration from such techniques.…”

Section: Context-based Signal Descriptors Of Heart-rate Variabilitymentioning

confidence: 99%

Context-based signal descriptors of heart-rate variability for anxiety assessment

Chatterjee

Stratou

Scherer

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we investigate the role of multiple contextbased heart-rate variability descriptors for evaluating a person's psychological health, specifically anxiety disorders. The descriptors are extracted from visually sensed heart-rate signals obtained during the course of a semi-structured interview with a virtual human and can potentially integrate question context as well. The proposed descriptors are motivated by prior related work and are constructed based on histogram-based approaches, time and frequency domain analysis of heart-rate variability. In order to contextualize our descriptors, we use information about the polarity and intimacy levels of the questions asked. Our experiments reveal that the descriptors, both with and without context, perform far better than chance in predicting anxiety. Further on, we perform at-a-par with the state-ofthe-art in predicting anxiety and other psychological disorders when we integrate the question context information into the descriptors.

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Histogram Equalization-Based Features for Speech, Music, and Song Discrimination

Cited by 19 publications

References 9 publications

Multiclass audio segmentation based on recurrent neural networks for broadcast domain data

Multiclass audio segmentation based on recurrent neural networks for broadcast domain data

Albayzín-2014 evaluation: audio segmentation and classification in broadcast news domains

Context-based signal descriptors of heart-rate variability for anxiety assessment

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