Results 11 to 20 of about 3,240 (204)

A sinusoidal signal reconstruction method for the inversion of the mel-spectrogram [PDF]

2021 IEEE International Symposium on Multimedia (ISM), 2021
The synthesis of sound via deep learning methods has recently received much attention. Some problems for deep learning approaches to sound synthesis relate to the amount of data needed to specify an audio signal and the necessity of preserving both the long and short time coherence of the synthesised signal.
Natsiou, Anastasia, O'Leary, Sean
openaire   +7 more sources

Mel spectrogram-based audio forgery detection using CNN

Signal, Image and Video Processing, 2022
Abstract In this time of technology, digital speech can be created and falsified by a very diverse of hardware and software technologies. Audio Copy-move forgery is an audio forgery technique that goals to create forged audio by hiding undesirable words or repeating wanted words in the identical speech.
Arda Ustubioglu, Beste Ustubioglu, Güzin Ulutas   +2 more
openaire   +3 more sources

Predicting the Remaining Time before Earthquake Occurrence Based on Mel Spectrogram Features Extraction and Ensemble Learning

Applied Sciences, 2023
Predicting the remaining time before the next earthquake based on seismic signals generated in a laboratory setting is a challenging research task that is of significant importance for earthquake hazard assessment.
Bo Zhang, Tao Xu, Wen Chen, Chongyang Zhang   +3 more
doaj   +2 more sources

Analisis Akurasi dan Waktu Proses Deteksi Sentimen Menggunakan Image Mel-Spectrogram

Techno.Com
Dalam upaya meningkatkan interaksi manusia-mesin, penelitian deteksi sentimen sudah banyak dilakukan peneliti untuk tujuan tersebut. Seiring dengan berkembangnya Mesin Pembelajaran, penelitian ini akan membandingkan kemampuan empat model klasifikasi ...
Jutono Gondohanindijo
doaj   +3 more sources

Comparative Study of Popular Deep Learning Models for Machining Roughness Classification Using Sound and Force Signals

Micromachines, 2021
This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Long ...
Binayak Bhandari
doaj   +2 more sources

GELP: GAN-Excited Linear Prediction for Speech Synthesis from Mel-Spectrogram [PDF]

Interspeech 2019, 2019
Recent advances in neural network -based text-to-speech have reached human level naturalness in synthetic speech. The present sequence-to-sequence models can directly map text to mel-spectrogram acoustic features, which are convenient for modeling, but present additional challenges for vocoding (i.e., waveform generation from the acoustic features ...
Juvela Lauri, Alku Paavo, Bollepalli Bajibabu, Yamagishi Junichi   +3 more
openaire   +6 more sources

Enhancing Embedded Space with Low–Level Features for Speech Emotion Recognition

Applied Sciences
This work proposes an approach that uses a feature space by combining the representation obtained in the unsupervised learning process and manually selected features defining the prosody of the utterances.
Lukasz Smietanka, Tomasz Maka
doaj   +2 more sources

Voice pathology identification using mel spectrogram features and deep learning [PDF]

Signal, Image and Video Processing
Abstract Voice pathology is very important in the identification of vocal disorders. Traditional methods of diagnosing voice disorders using voice pathology are expensive, time-consuming, and subjective. The study proposed the identification of normal and pathological voices using the Arabic Voice Pathology Database (AVPD).
Rab Nawaz Bashir, Muhammad Ali Shahid, Tahir Rashid, Muhammad Faheem 0003, Taoufik Saidani, Oumaima Saidani, Amjad Rehman Khan   +6 more
openaire   +3 more sources

Multi-Input Speech Emotion Recognition Model Using Mel Spectrogram and GeMAPS. [PDF]

Sensors (Basel), 2023
The existing research on emotion recognition commonly uses mel spectrogram (MelSpec) and Geneva minimalistic acoustic parameter set (GeMAPS) as acoustic parameters to learn the audio features.
Toyoshima I, Okada Y, Ishimaru M, Uchiyama R, Tada M.   +4 more
europepmc   +4 more sources

DMEL: The Differentiable Log-Mel Spectrogram as a Trainable Layer in Neural Networks

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
In this paper we present the differentiable log-Mel spectrogram (DMEL) for audio classification. DMEL uses a Gaussian window, with a window length that can be jointly optimized with the neural network.
John Martinsson, Maria Sandsten
core   +3 more sources