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Found 10 papers, 3 papers with code
Synergy between human and machine approaches to sound/scene recognition and processing: An overview of ICASSP special session
Machine Listening, as usually formalized, attempts to perform a task that is, from our perspective, fundamentally human-performable, and performed by humans.
Describing emotions with acoustic property prompts for speech emotion recognition
We investigate how the model can learn to associate the audio with the descriptions, resulting in performance improvement of Speech Emotion Recognition and Speech Audio Retrieval.
Audio Retrieval with WavText5K and CLAP Training
In this work, we propose a new collection of web audio-text pairs and a new framework for retrieval.
Adapting Task-Oriented Dialogue Models for Email Conversations
Additionally, the modular nature of the proposed framework allows plug-and-play for any future developments in both pre-trained language and task-oriented dialogue models.
NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback
Current research in dialogue systems is focused on conversational assistants working on short conversations in either task-oriented or open domain settings.
Improving weakly supervised sound event detection with self-supervised auxiliary tasks
To that extent, we propose a shared encoder architecture with sound event detection as a primary task and an additional secondary decoder for a self-supervised auxiliary task.
Interpreting glottal flow dynamics for detecting COVID-19 from voice
In the pathogenesis of COVID-19, impairment of respiratory functions is often one of the key symptoms.
Detection of COVID-19 through the analysis of vocal fold oscillations
Phonation, or the vibration of the vocal folds, is the primary source of vocalization in the production of voiced sounds by humans.
Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection
Weakly Labelled learning has garnered lot of attention in recent years due to its potential to scale Sound Event Detection (SED) and is formulated as Multiple Instance Learning (MIL) problem.
Attacker Behaviour Profiling using Stochastic Ensemble of Hidden Markov Models
For modelling we propose a novel semi-supervised algorithm called Fusion Hidden Markov Model (FHMM) which is more robust to noise, requires comparatively less training time, and utilizes the benefits of ensemble learning to better model temporal relationships in data.
