Recognizing emotion in speech and text using Deep Neural Networks and Mel-Frequency Cepstral Coefficients.

2021 ◽  
Vol 8 (5) ◽  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients

scholarly journals IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 676
Author(s):  
Andrej Zgank
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Markov Models ◽  
Audio Signal ◽  
Audio Signals ◽  
Mel Frequency Cepstral Coefficients ◽  
Animal Activity ◽  
The Impact ◽  
Acoustic Classification ◽  
Swarm Activity

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system.

scholarly journals Acoustic lung signals analysis based on Mel frequency cepstral coefficients and self-organizing maps

2016 ◽  
Vol 25 (43) ◽  
pp. 73-82
Author(s):  
Álvaro David Orjuela-Cañón ◽  
Hugo Fernando Posada-Quintero
Keyword(s):  
Neural Networks ◽  
Clustering Algorithm ◽  
Mel Frequency Cepstral Coefficients ◽  
Self Organizing Maps ◽  
Number Of Clusters ◽  
Supervised Training ◽  
Computer Aided ◽  
Unsupervised Training ◽  
Cepstral Coefficients ◽  
Self Organizing

This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained in an unsupervised or supervised manner. Both approaches were used in this work to compare the utility of this tool in lung signals studies. Results showed that with a supervised training, the classification reached rates of 85 % in accuracy. Unsupervised training was used for clustering tasks, and three clusters was the most adequate number for both supervised and unsupervised training. In general, SOM models can be used in lung signals as a strategy to diagnose systems, finding number of clusters in data, and making classifications for computer-aided decision making systems.

Voice Conversion System Based on Deep Neural Networks

2020 ◽  
Vol 17 (1) ◽  
pp. 316-321
Author(s):  
V. Naveena ◽  
Susmitha Vekkot ◽  
K. Jeeva Priya
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Objective Evaluation ◽  
Subjective Assessment ◽  
Voice Conversion ◽  
Mel Frequency Cepstral Coefficients ◽  
Listening Test ◽  
Conversion System ◽  
Maximum Improvement

The paper focuses on usage of deep neural networks for converting a person’s voice to another person’s voice, analogous to a mimic. The work in this paper introduces the concept of neural networks and deploys multi-layer deep neural networks for building a framework for voice conversion. The spectral Mel-Frequency Cepstral Coefficients (MFCCs) are converted using a 10-layer deep network while fundamental frequency (F0) conversion is accomplished by logarithmic Gaussian normalized transformation. MFCCs are subjected to inverse cepstral filtering while changes in F0 are incorporated using Pitch Synchronous OverLap Add (PSOLA) algorithm for re-synthesis. The results obtained are compared using Mel Cepstral Distortion (MCD) for objective evaluation while ABX-listening test is conducted for subjective assessment. Maximum improvement in MCD of 13.87% is obtained for female-to-male conversion while ABX-listening test indicates that female-to-male is closest to target with an agreement of 76.2%. The method achieves reasonably good performance compared to state-of-the-art using optimal resources and avoids requirement of highly complex computations.

scholarly journals Algorithms for Detection Gender Using Neural Networks

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Mel Frequency Cepstral Coefficients ◽  
Neural Architecture ◽  
Cepstral Coefficients ◽  
Gender Detection ◽  
The Voice ◽  
Gender Specific

In this paper, we investigate two neural architecture for gender detection tasks by utilizing Mel-frequency cepstral coefficients (MFCC) features which do not cover the voice related characteristics. One of our goals is to compare different neural architectures, multi-layers perceptron (MLP) and, convolutional neural networks (CNNs) for both tasks with various settings and learn the gender -specific features automatically.

PPG-based human identification using Mel-frequency cepstral coefficients and neural networks

2021 ◽  
Author(s):  
Ali I. Siam ◽  
Atef Abou Elazm ◽  
Nirmeen A. El-Bahnasawy ◽  
Ghada M. El Banby ◽  
Fathi E. Abd El-Samie
Keyword(s):  
Neural Networks ◽  
Human Identification ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients

scholarly journals Comparison between handwritten word and speech record in real-time using CNN architectures

2020 ◽  
Vol 10 (4) ◽  
pp. 4313
Author(s):  
Javier Orlando Pinzón-Arenas ◽  
Robinson Jiménez-Moreno
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Voice Recognition ◽  
Response Speed ◽  
Graphical Interface ◽  
Mel Frequency Cepstral Coefficients ◽  
Speed Factor ◽  
Learning Techniques ◽  
Cepstral Coefficients

This paper presents the development of a system of comparison between words spoken and written by means of deep learning techniques. There are used 10 words acquired by means of an audio function and, these same words, are written by hand and acquired by a webcam, in such a way as to verify if the two data match and show whether or not it is the required word. For this, 2 different CNN architectures were used for each function, where for voice recognition, a suitable CNN was used to identify complete words by means of their features obtained with mel frequency cepstral coefficients, while for handwriting, a faster R-CNN was used, so that it both locates and identifies the captured word. To implement the system, an easy-to-use graphical interface was developed, which unites the two neural networks for its operation. With this, tests were performed in real-time, obtaining a general accuracy of 95.24%, allowing showing the good performance of the implemented system, adding the response speed factor, being less than 200 ms in making the comparison.

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