Search Results - melspectrogram

Quad-Net: Melspectrogram Vocoder with Convolutional Layers Restricted by the Quadrature Mirror Filter for Perfect Reconstruction

Authors: Nam-Seok Song Joon-Hyuk Chang

Source: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :1-5

Classification of Human and Synthesized Audio in Marathi Speech using Melspectrogram Analysis and Convolutional Neural Network (CNN) Model

Authors: Atharv Choughule Riyaz Mullaji Drushtant Patil et al.

Source: 2024 International Conference on Communication, Computing, Smart Materials and Devices (ICCCSMD). :1-5

Melspectrogram Based Music Genre Classification System Using Vision Transformer

Authors: Paghdal Bansil Sapna Kushwah Avadh Kishor et al.

Source: 2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies (INSPECT). :1-6

Audio Emotion Mapping using Enhanced Techniques with Augmented Log-Melspectrograms

Authors: Eshika Jain Kanwarpartap Singh Gill Sonal Malhotra et al.

Source: 2024 4th Asian Conference on Innovation in Technology (ASIANCON). :1-4

A Symphony of Sentiments using Log-Melspectrogram Techniques for Emotional Classification

Authors: Eshika Jain Kanwarpartap Singh Gill Mukesh Kumar et al.

Source: 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). :825-829

Deep Learning-Driven Phonopneumographic Analysis For Pulmonary Disease Recognition Using Dft And Melspectrograms

Authors: Haritha J Gonuguntla Chandana Deekshitha G et al.

Source: International Journal For Multidisciplinary Research. 7

Emotional Resonance Unleashed by exploring Novel Audio Classification Techniques with Log- Melspectrogram Augmentation

Authors: Muskan Agarwal Deepak Upadhyay Kanwarpartap Singh Gill et al.

Source: 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. :1-5

Harmonizing Emotions: A Novel Approach to Audio Emotion Classification using Log-Melspectrogram with Augmentation

Authors: Muskan Agarwal Kanwarpartap Singh Gill Nitin Thapliyal et al.

Source: 2024 International Conference on Communication, Computing and Internet of Things (IC3IoT). :1-4

Nepali Text-to-Speech Synthesis using Tacotron2 for Melspectrogram Generation

Authors: Supriya Khadka Ranju G.C. Prabin Paudel et al.

Source: 2nd Annual Meeting of the ELRA/ISCA SIG on Under-resourced Languages (SIGUL 2023). :73-77

A Novel Melspectrogram Snippet Representation Learning Framework For Severity Detection of Chronic Obstructive Pulmonary Diseases

Authors: Udit Satija Arka Roy

Source: IEEE Transactions on Instrumentation and Measurement. 72:1-11

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 3. Good health

A comparative study of the spectrogram, scalogram, melspectrogram and gammatonegram time-frequency representations for the classification of lung sounds using the ICBHI database based on CNNs

Authors: Zakaria, Neili Kenneth, Sundaraj

Source: Biomedical Engineering / Biomedizinische Technik. 67:367-390

Subject Terms: 03 medical and health sciences, 0302 clinical medicine, Databases, Factual, 0202 electrical engineering, electronic engineering, information engineering, Humans, 02 engineering and technology, Lung, Respiratory Sounds

Access URL: https://pubmed.ncbi.nlm.nih.gov/35926850

Automatic Speech Recognition using the Melspectrogram-based method for English Phonemes

Authors: M Soundarya P R Karthikeyan Kirupa Ganapathy et al.

Source: 2022 International Conference on Computer, Power and Communications (ICCPC). :270-273

Additional Titles: Music Genre Classification Using Melspectrogram and UWT Coefficients

Index Terms: Departmental Bulletin Paper.

URL: https://chuo-u.repo.nii.ac.jp/records/14985

A Novel Melspectrogram Snippet Representation Learning Framework for Severity Detection of Chronic Obstructive Pulmonary Diseases

Authors: Roy, Arka Satija, Udit

Source: IEEE Transactions on Instrumentation and Measurement ; volume 72, page 1-11 ; ISSN 0018-9456 1557-9662

Availability: https://doi.org/10.1109/tim.2023.3256468
http://xplorestaging.ieee.org/ielx7/19/10012124/10068273.pdf?arnumber=10068273

A comparative study of the spectrogram, scalogram, melspectrogram and gammatonegram time-frequency representations for the classification of lung sounds using the ICBHI database based on CNNs

Authors: Neili, Zakaria Sundaraj, Kenneth

File Description: text

Relation: http://eprints.utem.edu.my/id/eprint/26592/2/2022%20ZAKI%20BMT_COMPRESSED.PDF; Neili, Zakaria and Sundaraj, Kenneth (2022) A comparative study of the spectrogram, scalogram, melspectrogram and gammatonegram time-frequency representations for the classification of lung sounds using the ICBHI database based on CNNs. Biomedizinische Technik, 67 (5). pp. 367-390. ISSN 1862-278X

Availability: http://eprints.utem.edu.my/id/eprint/26592/
http://eprints.utem.edu.my/id/eprint/26592/2/2022%20ZAKI%20BMT_COMPRESSED.PDF
https://www.degruyter.com/document/doi/10.1515/bmt-2022-0180/html?lang=en

Convolutional Neural Networks for Classification of Voice Qualities from Speech and Neck Surface Accelerometer Signals

Authors: Javanmardi, Farhad Alku Paavo et al.

Source: Annual Conference of the International Speech Communication Association. :5253-5257

Subject Terms: computational paralinguistics, neck surface accelerometer, ta213, Melspectrogram, CNNs, Voice quality

Access URL: http://juuli.fi/Record/0394348022

Convolutional Neural Networks for Classification of Voice Qualities from Speech and Neck Surface Accelerometer Signals

Authors: Javanmardi Farhad Alku Paavo Kadiri Sudarsana et al.

Contributors: Javanmardi Farhad Alku Paavo Kadiri Sudarsana et al.

Source: Interspeech 2022. :5253-5257

Subject Terms: computational paralinguistics, neck surface accelerometer, 03 medical and health sciences, 0103 physical sciences, Melspectrogram, CNNs, Voice quality, 0305 other medical science, 01 natural sciences

File Description: application/pdf

Access URL: https://aaltodoc.aalto.fi/handle/123456789/117723

A Novel Melspectrogram Snippet Representation Learning Framework For Severity Detection of Chronic Obstructive Pulmonary Diseases

Authors: Roy, Arka Satija, Udit

Availability: https://doi.org/10.36227/techrxiv.21758660
https://ndownloader.figshare.com/files/38613821

Reconhecimento de Dígitos de Medidores de Energia por meio da Voz no Contexto de um Aplicativo de Autoleitura / Digit Recognition of Energy Meters through Voice in the Context of an Authentication Application

Authors: Mendes, Paulo Renato Conceição Rocha, Carlos Vinicios Martins Paiva, Anderson Matheus Passos et al.

Source: Brazilian Applied Science Review; Vol. 4 No. 3 (2020); 1976-1989
Brazilian Applied Science Review; v. 4 n. 3 (2020); 1976-1989
Brazilian Applied Science Review
Brazilian Journals Publicações de Periódicos e Editora Ltda
instacron:FIEP

Subject Terms: Aprendizado de Máquina, Consumo de Energia, MFCC, SVM, 0202 electrical engineering, electronic engineering, information engineering, Reconhecimento, 02 engineering and technology, 0101 mathematics, MelSpectrogram, Autoleitura, Processamento de Áudio, 01 natural sciences

File Description: application/pdf

Access URL: https://www.brazilianjournals.com/index.php/BASR/article/download/11999/10031
https://ojs.brazilianjournals.com.br/ojs/index.php/BASR/article/view/11999

Comparative Study of Mfcc and Mel Spectrogram for Raga Classification Using CNN

Authors: Dipti Joshi Jyoti Pareek Pushkar Ambatkar

Subject Terms: Raga identification, Music Information Retrieval, Feature extraction, Mfcc, Melspectrograms, CNN

Relation: https://zenodo.org/records/13309959; oai:zenodo.org:13309959; https://doi.org/10.17485/IJST/v16i11.1809

Availability: https://doi.org/10.17485/IJST/v16i11.1809
https://zenodo.org/records/13309959
https://indjst.org/articles/comparative-study-of-mfcc-and-mel-spectrogram-for-raga-classification-using-cnn