Automated Detection of COVID-19 Cough Sound using Mel-Spectrogram Images and Convolutional Neural Network [PDF]
, 2023 COVID-19 is a new disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) variant. The initial symptoms of the disease commonly include fever (83-98%), fatigue or myalgia, dry cough (76-82%), and shortness of breath (31-55 ...
Indriani, Fatma +4 more
core +1 more source
Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network
Applied Sciences, 2022 Pipeline transportation is the main method for long-distance gas transportation; however, ponding in the pipeline can affect transportation efficiency and even cause corrosion to the pipeline in some cases.
Dan Yang +3 more
doaj +1 more source
Deep transfer learning-based bird species classification using mel spectrogram images. [PDF]
PLoS OneThe classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and ...
Baowaly MK +7 more
europepmc +3 more sources
Deep learning bird song recognition based on MFF-ScSEnet
Ecological Indicators, 2023 Bird diversity plays an important role in ecological balance, and bird song identification is of great practical significance. The spectrum generated by feature extraction shows good performance on classification.
Shipeng Hu +5 more
doaj +1 more source
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions [PDF]
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 Accepted to ICASSP ...
Jonathan Shen +12 more
openaire +2 more sources
Iterating over 28,500 steps of the Mel-spectrogram.
, 2023 Iterating over 28,500 steps of the Mel-spectrogram.
Xianyou Zhu (11588965) +1 more
core +1 more source
Mel-spectrogram features for acoustic vehicle detection and speed estimation
2022 26th International Conference on Information Technology (IT), 2022 The paper addresses acoustic vehicle detection and speed estimation from single sensor measurements. We predict the vehicle's pass-by instant by minimizing clipped vehicle-to-microphone distance, which is predicted from the mel-spectrogram of input audio, in a supervised learning approach.
Nikola Bulatovic, Slobodan Djukanovic
openaire +2 more sources
An investigation of the reconstruction capacity of stacked convolutional autoencoders for log-mel-spectrograms [PDF]
2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2022 In audio processing applications, the generation of expressive sounds based on high-level representations demonstrates a high demand. These representations can be used to manipulate the timbre and influence the synthesis of creative instrumental notes. Modern algorithms, such as neural networks, have inspired the development of expressive synthesizers ...
Anastasia Natsiou +2 more
openaire +2 more sources
An improved ViT model for music genre classification based on mel spectrogram. [PDF]
PLoS OneAutomating the task of music genre classification offers opportunities to enhance user experiences, streamline music management processes, and unlock insights into the rich and diverse world of music. In this paper, an improved ViT model is proposed to extract more comprehensive music genre features from Mel spectrograms by leveraging the strengths of ...
Wu P +6 more
europepmc +4 more sources
CycleGAN-VC3: Examining and Improving CycleGAN-VCs for Mel-Spectrogram Conversion [PDF]
Interspeech 2020, 2020 Non-parallel voice conversion (VC) is a technique for learning mappings between source and target speeches without using a parallel corpus. Recently, cycle-consistent adversarial network (CycleGAN)-VC and CycleGAN-VC2 have shown promising results regarding this problem and have been widely used as benchmark methods.
Takuhiro Kaneko +3 more
openaire +2 more sources