Improvement of Speech/Music Classification for 3GPP EVS Based on LSTM

Kang, Sang-Ick; Lee, Sang‐Min

doi:10.3390/sym10110605

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…The LSTM layer can selectively forget and retain information through the gating mechanism, effectively solve the gradient disappearance or explosion problem of RNN, and is suitable for the classification of timeseries signals. [55,56] The Bi-LSTM model can fuse forward and backward semantic information from two LSTM layers to provide complete contextual state information, which is beneficial for sound recognition. The Bi-LSTM output was used as input to the fully connected layer and was recognized and classified by the SoftMax activation function.…”

Section: Applications In Sound Signal Collectionmentioning

confidence: 99%

A General Strategy for Rapidly Optimizing Wearable Resistive Pressure Sensors

Wen

Nie

Fan

et al. 2023

Adv Elect Materials

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Due to the complicated relationship between wearable electronics performance and various parameters, months even years are needed to obtain a desired sensor by random and time-consuming trial-and-error methods. Herein, a general analytic model based on the micro-element division and equivalent circuit is presented to guide a rapid optimizing strategy for wearable resistive pressure sensors, which is like the method always used in the traditional design of the metal-oxide-semiconductor field-effect transistor for integrated circuits. The quantitative relationship between the sensitivity and related parameters is declared in the presented model, and the optimized parameters are achieved to design a sensor. The demanded ultra-highly sensitive pressure sensor is successfully designed and optimized in minutes based on the built model, and the fabricated sensor is applied in a voice real-time recognition system to obtain 100% recognition accuracy. The on-demand and agile development strategy paves a promising way to greatly accelerate the transition from random and time-consuming to the controllable design of wearable electronics.

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Section: Applications In Sound Signal Collectionmentioning

confidence: 99%

A General Strategy for Rapidly Optimizing Wearable Resistive Pressure Sensors

Wen

Nie

Fan

et al. 2023

Adv Elect Materials

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“…The frequently applied machine learning methods include K-Nearest Neighbor (k-NN) [1,2,3,4,5,6], Support Vector Machine (SVM) [7,8,9], Long Short-Term Memory (LSTM) [10,11,12,13,14,15], and Convolutional Neural Network (CNN) and its variants [4,16,17,18,19,20,21,22,23,24], and ensemble models [25,26,27,28,29,30,31,18]. However, a majority of research and experiments done within the field of musical instrument recognition or music classification are targeted at those belonging to western culture, mostly of European and North American origin.…”

Section: Introductionmentioning

confidence: 99%

Deep Chnet 1-D CNN using raw audio waveform for recognizing traditional Chinese musical instrument

2023

ACE

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The classification of music information using various deep learning models is increasingly popular in the field of Music Information Retrieval research. However, as most proposed works focus on western music and musical instruments, little attention is given to traditional Chinese music. This paper proposes a 1-D Convolutional Neural Network (1-D CNN) using only raw audio waveform as input, to undertake the task of traditional Chinese musical instruments classification. This paper starts with a review of the current state of research on the related field, then discuss the proposed model and its data in detail, followed by its performance metrics and then a conclusion on the experiment. The result shows that 1-D CNN provides competitive and even superior results when compared to its 2-D versions as well as when compared to traditional models.

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“…This special issue of Symmetry entitled "Emerging Approaches and Advances in Big Data" consists of 17 papers [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] that all present research in the emerging area of Big Data.…”

mentioning

confidence: 99%