Speech activity detection in highly variable acoustic conditions is a challenging task. Many
approaches to detect speech activity in such conditions involve an inherent knowledge of …

Audio signals represent a wide diversity of acoustic events, from background environmental
noise to spoken communication. Machine learning models such as neural networks have …

Conventional methods for finding audio in databases typically search text labels, rather than
the audio itself. This can be problematic as labels may be missing, irrelevant to the audio …

This report presents our audio event detection system submitted for Task 2," Detection of
rare sound events", of DCASE 2017 challenge. The proposed system is based on …

Audio-Text retrieval takes a natural language query to retrieve relevant audio files in a
database. Conversely, Text-Audio retrieval takes an audio file as a query to retrieve relevant …

Audio Word2Vec offers vector representations of fixed dimensionality for variable-length
audio segments using Sequence to-sequence Autoencoder (SA). These vector …

Despite recent progress in text-to-audio (TTA) generation, we show that the state-of-the-art
models, such as AudioLDM, trained on datasets with an imbalanced class distribution, such …

Speech, Music and Noise classification/segmentation is an important preprocessing step for
audio processing/indexing. To this end, we propose a novel 1D Convolutional Neural …

Information retrieval from brain responses to auditory and visual stimuli has shown success
through classification of song names and image classes presented to participants while …

Deep Learning has become state of the art in visual computing and continuously emerges
into the Music Information Retrieval (MIR) and audio retrieval domain. In order to bring …