ICASSP 2020 Documents | SigPort

ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

Advanced Search

Events on SigPort

Source Coding of Audio Signals with a Generative Model

Audio-based Detection of Explicit Content in Music

Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection

I-VECTOR TRANSFORMATION USING K-NEAREST NEIGHBORS FOR SPEAKER VERIFICATION

VOICE BASED CLASSIFICATION OF PATIENTS WITH AMYOTROPHIC LATERAL SCLEROSIS, PARKINSON'S DISEASE AND HEALTHY CONTROLS WITH CNN-LSTM USING TRANSFER LEARNING

Application Informed Motion Signal Processing for Finger Motion Tracking Using Wearable Sensors

SPEECH RECOGNITION MODEL COMPRESSION

Acoustic Matching by Embedding Impulse Responses

CROSS LINGUAL TRANSFER LEARNING FOR ZERO-RESOURCE DOMAIN ADAPTATION

On the Stability of Polynomial Spectral Graph Filters

MULTI-HEAD ATTENTION FOR SPEECH EMOTION RECOGNITION WITH AUXILIARY LEARNING OF GENDER RECOGNITION

Combining cGAN and MIL for Hotspot Segmentation in Bone Scintigraphy

Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning

Optimizing Bayesian HMM Based x-vector Clustering for theSecond DIHARD Speech Diarization Challenge

Detecting Mismatch between Text Script and Voice-over Using Utterance Verification Based on Phoneme Recognition Ranking

Detection of Malicious VBScript Using Static and Dynamic Analysis with Recurrent Deep Learning

Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption

'TEXCEPTION: A Character/Word-Level Deep Learning Model for Phishing URL Detection

A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition

Fast and High-Quality Singing Voice Synthesis System based on Convolutional Neural Networks

Pages