Results 91 to 100 of about 7,229,432 (222)
full_cough_2_d_coswara_coughvid_features
Features extracted from Coswara and COUGHVID datasets: Mel-spectrogram, MFCC 13, 26 ...
Duong, Van Binh
core +1 more source
Impulsive acoustic event classification based on spectrogram representations remains challenging due to the non-stationary and broadband characteristics of transient signals.
Pafan Doungpaisan, Peerapol Khunarsa
doaj +1 more source
Overview of the proposed Gate‐Align‐SED, including two stages of training: (1) Mean‐Teacher SSL Training; and (2) Enhancer Model Training. In complex real‐world environments such as disaster monitoring, effective sound event detection (SED) is often hindered by the presence of noise and limited labeled data.
Jieli Chen +4 more
wiley +1 more source
This dataset is a collection of mel-spectrogram features extracted from Indian regional music containing the following languages: Hindi, Gujarati, Marathi, Konkani, Bengali, Oriya, Kashmiri, Assamese, Nepali, Konyak, Manipuri, Khasi & Jaintia, Tamil ...
Yeshwant Singh, Anupam Biswas
core +1 more source
IntroductionMusical instrument recognition is a critical component of music information retrieval (MIR), aimed at identifying and classifying instruments from audio recordings.
Rujia Chen +2 more
doaj +1 more source
Application of Music Data Visualization Technology in Music Appreciation Teaching
The simulation environment is used to simulate real‐world music appreciation scenarios. DL is employed to preprocess music data, extract features, and identify rhythm information, which is then associated with visual design parameters to construct a parametric model.
Xiaowei Chen
wiley +1 more source
Indian Semi-Classical Music Dataset
This dataset is a collection of mel-spectrogram features extracted from Indian semi-classical music containing the following 9 semi-classical styles: Bhajan, Chaiti, Dadra, Ghazal, Kajri, Natya Sangeet, Qawwali, Tappa, Thumri.
Yeshwant Singh +2 more
core +1 more source
A lightweight dual‐branch neural network with cross‐attention fusion (LDCNN‐CF) integrates Mel‐spectrograms with psychoacoustic parameters to predict electric toothbrush acoustic comfort. The model achieves human‐like accuracy (MAE = 0.82, R2 = 0.84) with only 0.42 M parameters and identifies roughness as the dominant predictor of discomfort.
Yang Zhang
wiley +1 more source
Diffusion-Based Mel-Spectrogram Enhancement for Personalized Speech Synthesis with Found Data
Creating synthetic voices with found data is challenging, as real-world recordings often contain various types of audio degradation. One way to address this problem is to pre-enhance the speech with an enhancement model and then use the enhanced data for
Liu, Wei, Lee, Tan, Tian, Yusheng
core +1 more source
On Zero-Shot Multi-Speaker Text-to-Speech Using Deep Learning [PDF]
This thesis explores various aspects of zero-shot multi-speaker text-to-speech (TTS) synthesis using deep learning to create an effective system. A deep learning model for zero-shot multi-speaker TTS uses text and speaker identity as input to generate ...
Kandarkar, Pradnya
core

