Multi-channel spectrograms for speech processing applications using deep learning methods

Arias-Vergara, Tomás; Klumpp, Philipp; Vásquez-Correa, Juan Camilo; Nöth, Elmar; Orozco-Arroyave, Juan Rafael; Schuster, Maria

doi:10.1007/s10044-020-00921-5

Cited by 54 publications

(27 citation statements)

References 27 publications

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“…The experimental results show that MGANet is superior to the most advanced baseline. In future work, it could be expanded to emotion recognition, speech processing, non-stationary signal analysis, and other fields [ 28 , 29 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG

Wang

Guo

Shen

et al. 2022

Sensors

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Graph neural networks have been successfully applied to sleep stage classification, but there are still challenges: (1) How to effectively utilize epoch information of EEG-adjacent channels owing to their different interaction effects. (2) How to extract the most representative features according to confused transitional information in confused stages. (3) How to improve classification accuracy of sleep stages compared with existing models. To address these shortcomings, we propose a multi-layer graph attention network (MGANet). Node-level attention prompts the graph attention convolution and GRU to focus on and differentiate the interaction between channels in the time-frequency domain and the spatial domain, respectively. The multi-head spatial-temporal mechanism balances the channel weights and dynamically adjusts channel features, and a multi-layer graph attention network accurately expresses the spatial sleep information. Moreover, stage-level attention is applied to easily confused sleep stages, which effectively improves the limitations of a graph convolutional network in large-scale graph sleep stages. The experimental results demonstrated classification accuracy; MF1 and Kappa reached 0.825, 0.814, and 0.775 and 0.873, 0.801, and 0.827 for the ISRUC and SHHS datasets, respectively, which showed that MGANet outperformed the state-of-the-art baselines.

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Section: Related Workmentioning

confidence: 99%

Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG

Wang

Guo

Shen

et al. 2022

Sensors

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“…We are expecting that each branch will become an expert at distinguishing sound from a certain string. e characteristics recovered by a common convolutional layer will be used by all six branches [30][31][32]. is can be better understood by looking at the summarized plot of our model given in Figure 3.…”

Section: Model Architecturementioning

confidence: 99%

Transfer Learning for Audio Waveform to Guitar Chord Spectrograms Using the Convolution Neural Network

Jadhav

Patel²,

Jhaveri

et al. 2022

Mobile Information Systems

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Automatic chord recognition has always been approached as a broad music audition task. The desired output is a succession of time-aligned discrete chord symbols, such as GMaj and Asus2. Automatic music transcription is the process of converting a musical recording into a human-readable and interpretable representation. When dealing with polyphonic sounds or removing certain limits, automatic music transcription remains a difficult undertaking. A guitar, for example, presents a greater challenge, as guitarists can play the same note in a variety of places. The study makes use of CNN functionality to generate the guitar tab; initially, the constant-Q transform was used to turn the input audio file into short time spectrograms that the CNN model utilises to analyse the chord. The paper developed a method for extracting chord sequences and notes from audio recordings of solo guitar performances. For intervals in the supplied audio, the proposed approach outputs chord names and fret-board notes. The model described here has been refined to achieve an accuracy of 88.7%. The model’s ability to properly tag audio clips is an incredible advancement.

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“…Recently, researchers have attracted significant attention to Deep Learning (DL) [11][12][13][14] owing to its numerous applications in speech processing [15], natural language processing [16], and CV [17,18]. In video recognition [19] and large-scale images, a model of DL so-called convolutional neural network (CNN) has lately attained several encouraging results.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification

et al. 2022

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In the last decade, there has been a surge of interest in addressing complex Computer Vision (CV) problems in the field of face recognition (FR). In particular, one of the most difficult ones is based on the accurate determination of the ethnicity of mankind. In this regard, a new classification method using Machine Learning (ML) tools is proposed in this paper. Specifically, a new Deep Learning (DL) approach based on a Deep Convolutional Neural Network (DCNN) model is developed, which outperforms a reliable determination of the ethnicity of people based on their facial features. However, it is necessary to make use of specialized high-performance computing (HPC) hardware to build a workable DCNN-based FR system due to the low computation power given by the current central processing units (CPUs). Recently, the latter approach has increased the efficiency of the network in terms of power usage and execution time. Then, the usage of field-programmable gate arrays (FPGAs) was considered in this work. The performance of the new DCNN-based FR method using FPGA was compared against that using graphics processing units (GPUs). The experimental results considered an image dataset composed of 3141 photographs of citizens from three distinct countries. To our knowledge, this is the first image collection gathered specifically to address the ethnicity identification problem. Additionally, the ethnicity dataset was made publicly available as a novel contribution to this work. Finally, the experimental results proved the high performance provided by the proposed DCNN model using FPGAs, achieving an accuracy level of 96.9 percent and an F1 score of 94.6 percent while using a reasonable amount of energy and hardware resources.

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Multi-channel spectrograms for speech processing applications using deep learning methods

Cited by 54 publications

References 27 publications

Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG

Multi-Layer Graph Attention Network for Sleep Stage Classification Based on EEG

Transfer Learning for Audio Waveform to Guitar Chord Spectrograms Using the Convolution Neural Network

Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification

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