Results 21 to 30 of about 2,773 (174)

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

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, Mengzhou Xiong, Tao Wang, Guangtao Lu   +3 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, Ruoming Pang, Ron J. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang 0033, Yuxuan Wang 0002, R. J. Skerry-Ryan, Rif A. Saurous, Yannis Agiomyrgiannakis, Yonghui Wu   +12 more
openaire   +3 more sources

cMelGAN: An Efficient Conditional Generative Model Based on Mel Spectrograms

CoRR, 2022
Analysing music in the field of machine learning is a very difficult problem with numerous constraints to consider. The nature of audio data, with its very high dimensionality and widely varying scales of structure, is one of the primary reasons why it is so difficult to model.
Tracy Qian, Jackson Kaunismaa, Tony Chung   +2 more
openaire   +3 more sources

Data-Dependent Feature Extraction Method Based on Non-Negative Matrix Factorization for Weakly Supervised Domestic Sound Event Detection

Applied Sciences, 2021
In this paper, feature extraction methods are developed based on the non-negative matrix factorization (NMF) algorithm to be applied in weakly supervised sound event detection. Recently, the development of various features and systems have been attempted
Seokjin Lee, Minhan Kim, Seunghyeon Shin, Sooyoung Park, Youngho Jeong   +4 more
doaj   +1 more source

Pre-Configured Deep Convolutional Neural Networks with Various Time-Frequency Representations for Biometrics from ECG Signals

Applied Sciences, 2019
We evaluated electrocardiogram (ECG) biometrics using pre-configured models of convolutional neural networks (CNNs) with various time-frequency representations.
Yeong-Hyeon Byeon, Keun-Chang Kwak
doaj   +1 more source

Feature selection for emotion recognition in speech: a comparative study of filter and wrapper methods [PDF]

PeerJ Computer Science
Feature selection is essential for enhancing the performance and reducing the complexity of speech emotion recognition models. This article evaluates various feature selection methods, including correlation-based (CB), mutual information (MI), and ...
Alaa Altheneyan, Aseel Alhadlaq
doaj   +2 more sources

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   +3 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, Hirokazu Kameoka, Kou Tanaka, Nobukatsu Hojo   +3 more
openaire   +4 more sources

Mel-Refine: A Plug-and-Play Approach to Refine Mel-Spectrogram in Audio Generation

2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Text-to-audio (TTA) model is capable of generating diverse audio from textual prompts. However, most mainstream TTA models, which predominantly rely on Mel-spectrograms, still face challenges in producing audio with rich content. The intricate details and texture required in Mel-spectrograms for such audio often surpass the models' capacity, leading to
Hongming Guo, Ruibo Fu, Yizhong Geng, Shuchen Shi, Tao Wang 0074, Chunyu Qiang, Ya Li 0001, Zhengqi Wen, Yukun Liu, Xuefei Liu, Chenxing Li   +10 more
openaire   +5 more sources