Results 141 to 150 of about 598 (156)

Cochleagram-based identification of electronic disguised voice with pitch scaling in the noisy environment

Proceedings of the ACM Turing Celebration Conference - China, 2019
Audio editing software makes voice camouflage easily. That threats to the security and authenticity of audio. Whether the audio forensics can identify voice disguised by software has become an important issue. At the same time, since the audio used in daily life always contains noise, the other key point is improving the anti-noise performance.
Wen Dou, Hongxia Wang 0001, Ruixi Yang
openaire   +1 more source

A Semi-supervised Speaker Identification Method for Audio Forensics Using Cochleagrams

2017
The general task in speaker identification for audio forensics is to identify the unknown speaker within an audio proof, who is suspected of a crime. Here, the voice of each person within a group of suspects is compared to the audio proof with the aim to determining which of them corresponds to the source.
Steven Camacho, Diego Renza, Dora M. Ballesteros L.   +2 more
openaire   +1 more source

Broadband continuous wave source localization via pair-wise, cochleagram processing

The Journal of the Acoustical Society of America, 2005
A pair-wise processor has been developed for the passive localization of broadband continuous-wave underwater sources. The algorithm uses sparse hydrophone arrays and does not require previous knowledge of the source signature. It is applicable in multiple source situations. A spectrogram/cochleagram version of the algorithm has been developed in order
Eva-Marie Nosal, L. Neil Frazer
openaire   +1 more source

Indian Language Identification Using Cochleagram Based Texture Descriptors and ANN Classifier

2018 15th IEEE India Council International Conference (INDICON), 2018
For a human translator, to identify many languages and to translate them accurately is a difficult task. The topic of identification of a spoken languages has become an active research field in the community of speech processing. In this paper, a two-stage identification approach for six Indian languages viz: Marathi, Hindi, Telugu, Malayalam, Kannada &
Ashish H. Jog, Omkar A. Jugade, Atharva S. Kadegaonkar, Gajanan K. Birajdar   +3 more
openaire   +1 more source

Deep learning approach of murmur detection using Cochleagram

Biomedical Signal Processing and Control, 2022
Sangita Das, Saurabh Pal 0003, Madhuchhanda Mitra   +2 more
openaire   +1 more source

Computationally Efficient Classification of Audio Events Using Binary Masked Cochleagrams

2019
In this work, a computationally efficient technique for acoustic events classification is presented. The approach is based on cochleagram structure by identification of dominant time-frequency units. The input signal is splitting into frames, then cochleagram is calculated and masked by the set of masks to determine the most probable audio class.
openaire   +1 more source

Cochleagram-based audio pattern separation using two-dimensional non-negative matrix factorization with automatic sparsity adaptation

The Journal of the Acoustical Society of America, 2014
An unsupervised single channel audio separation method from pattern recognition viewpoint is presented. The proposed method does not require training knowledge and the separation system is based on non-uniform time-frequency (TF) analysis and feature extraction.
Gao B, Woo WL, Khor LC
openaire   +3 more sources

Affect Recognition Through Scalogram and Multi-Resolution Cochleagram Features

Interspeech 2021, 2021
Fasih Haider, Saturnino Luz
openaire   +1 more source

Lightweight Noise Robust Spoofing Attack Detection using Cochleagram and ResNet Amalgamated Features

Physica Scripta
Abstract Speaker verification, like other biometric technologies, is vulnerable to spoofing attacks. An attacker impersonates a specific target speaker using impersonation, replay, Text-to-Speech (TTS), or Voice conversion (VC) techniques to gain unauthorized access to the system.
Nidhi Chakravarty, Mohit Dua
openaire   +1 more source

Machine Hearing for Industrial Acoustic Monitoring using Cochleagram and Spiking Neural Network

2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2022
Yu Zhang 0001, Shirin Dora, Miguel Martínez-García, Saugat Bhattacharya   +3 more
openaire   +1 more source