Results 131 to 140 of about 7,229,432 (222)

Klasifikasi Pengucapan Huruf Hijaiyah Berbasis Android Menggunakan CNN dengan Fitur Mel-Spectrogram

Mastery of Hijaiyah letters is a fundamental basis in learning the Qur'an, but data from the IIQ Community Service Institute 2021/2022 shows that 72.25% of the 3,111 Muslims tested have not been able to read the Qur'an properly.
Listyorini, Tri, Muqsith, Fawwaz Ijlal, Supriyati, Endang   +2 more
core   +1 more source

Texture Feature and Mel-Spectrogram Analysis for Music Sound Classification

International Journal of Advanced Computer Science and Applications
org
M. ElAlami, S. Tobar, S. M. Khater, Eman. A. Esmaeil   +3 more
semanticscholar   +1 more source

Exhaustive benchmark of architectural variations with respect to the logmelspec-CNN baseline, on a task of binary classification of presence vs. absence of a bird in audio clips.

, 2019
Dot colors represent folds in BirdVox-70k. GDA: geometrical data augmentation. logmelspec: log-mel-spectrogram. PCEN: per-channel energy normalization. MoE: mixture of experts. AT: adaptive threshold.
Andrew Farnsworth (401325), Steve Kelling (337133), Vincent Lostanlen (5803529), Juan Pablo Bello (3359759), Justin Salamon (634133)   +4 more
core   +1 more source

Spectrogram Features for Audio and Speech Analysis

Applied Sciences
Spectrogram-based representations have grown to dominate the feature space for deep learning audio analysis systems, and are often adopted for speech analysis also.
Ian McLoughlin, Lam Pham, Yan Song, Xiaoxiao Miao, Huy Phan, Pengfei Cai, Qing Gu, Jiang Nan, Haoyu Song, Donny Soh   +9 more
doaj   +1 more source

Audio Conversion for Music Genre Classification Using Short-Time Fourier Transform and Inception V3

Jurnal Elkomika
This research examines the development of music genres and technological applications in music genre recognition through the MIR (Music Information Retrieval) approach.
DEWI ROSMALA, MOHAMMAD NOER FADHILAH
doaj   +1 more source

VQTTS: High-Fidelity Text-to-Speech Synthesis with Self-Supervised VQ Acoustic Feature

The mainstream neural text-to-speech(TTS) pipeline is a cascade system, including an acoustic model(AM) that predicts acoustic feature from the input transcript and a vocoder that generates waveform according to the given acoustic feature.
Du, Chenpeng, Guo, Yiwei, Chen, Xie, Yu, Kai   +3 more
core   +2 more sources

A hybrid CNN and reinforcement learning framework for speaker identification using Mel-Spectrogram and continuous wavelet transform features. [PDF]

Sci Rep
Heir FM, Najafzadeh H, Erfani S.
europepmc   +1 more source

Firearm classification from acoustic signals using combined mel spectrogram, MFCC, LFCC, and CRNN networks. [PDF]

Sci Rep
Elkarous L, Jeridi MH, Dhouibi M.
europepmc   +1 more source

Optimized Music Classification with a Hybrid VGG16-RNN Using Mel-Spectrogram and MFCC Features

Music classification using deep neural networks has gained a lot of attention in recent years. This is due to the difficult task of capturing every essential aspect of music in features and interpretability of classifiers.
Ashraf, Mohsin, Ashraf , Saima
core   +1 more source

Speech Emotion Recognition using Mel Spectrogram and Convolutional Neural Networks (CNN)

Procedia Computer Science
Vidhi Sareen, Seeja K.R
semanticscholar   +1 more source