Results 151 to 160 of about 4,974,855 (283)
Abstract Whatever the circumstances, the separation of infants from their mothers at birth is a traumatic experience for all concerned. The paper reports on a study designed to improve practice in this highly sensitive area. An analysis of data collected through semi‐structured interviews with 38 mothers who had experienced removal at birth identified ...
Claire Mason+2 more
wiley +1 more source
Learning Source Disentanglement in Neural Audio Codec [PDF]
Neural audio codecs have significantly advanced audio compression by efficiently converting continuous audio signals into discrete tokens. These codecs preserve high-quality sound and enable sophisticated sound generation through generative models trained on these tokens.
arxiv
Database of domestic sounds for evaluating audio signals.
Kenji Kurakata+3 more
openalex +2 more sources
Non-blind audio-watermarking using compression-expansion of signals [PDF]
Say Wei Foo
openalex +1 more source
Deep Learning for Lossless Audio Compression
Audio and speech compression techniques are used to reduce the storage of these data in the required space and the transmission rate of these data in the communication and network systems.
Ali A. Obaid, Hasan M. Kadhim
doaj +1 more source
Bayesian extensions to non-negative matrix factorisation for audio signal modelling [PDF]
Tuomas Virtanen+2 more
openalex +1 more source
Abstract The first experience of medical students in the dissecting room (DR) likely influences professional identity formation (PIF). Sparse data exist exploring how exposure to the DR and body donors without undertaking dissection influences PIF, or how culture may influence this experience.
Jacob Madgwick+2 more
wiley +1 more source
DSCLAP: Domain-Specific Contrastive Language-Audio Pre-Training [PDF]
Analyzing real-world multimodal signals is an essential and challenging task for intelligent voice assistants (IVAs). Mainstream approaches have achieved remarkable performance on various downstream tasks of IVAs with pre-trained audio models and text models. However, these models are pre-trained independently and usually on tasks different from target
arxiv
“Lives and times”: The case for qualitative longitudinal research in anatomical sciences education
Abstract Qualitative longitudinal research (QLR) focuses on changes in perceptions, interpretations, or practices through time. Despite longstanding traditions in social science, QLR has only recently appeared in anatomical sciences education (ASE).
Charlotte E. Rees, Ella Ottrey
wiley +1 more source
Towards Privacy-Preserving Audio Classification Systems [PDF]
Audio signals can reveal intimate details about a person's life, including their conversations, health status, emotions, location, and personal preferences. Unauthorized access or misuse of this information can have profound personal and social implications.
arxiv