Search Results for author:
Found 15 papers, 6 papers with code
Pengi: An Audio Language Model for Audio Tasks
We introduce Pengi, a novel Audio Language Model that leverages Transfer Learning by framing all audio tasks as text-generation tasks.
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.
PAM: Prompting Audio-Language Models for Audio Quality Assessment
Here, we exploit this capability and introduce PAM, a no-reference metric for assessing audio quality for different audio processing tasks.
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.
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.
Training Audio Captioning Models without Audio
During inference, the text encoder is replaced with the pretrained CLAP audio encoder.
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.
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.
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.
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.
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.
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.
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.
Prompting Audios Using Acoustic Properties For Emotion Representation
In this work, we address the challenge of automatically generating these prompts and training a model to better learn emotion representations from audio and prompt pairs.
LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model
We hypothesize that for attacks to be transferrable, it is sufficient if the proxy can approximate the target model in the neighborhood of the harmful query.
