Search Results for author: Huaming Wang

Found 19 papers, 6 papers with code

PAM: Prompting Audio-Language Models for Audio Quality Assessment

1 code implementation1 Feb 2024 Soham Deshmukh, Dareen Alharthi, Benjamin Elizalde, Hannes Gamper, Mahmoud Al Ismail, Rita Singh, Bhiksha Raj, Huaming Wang

Here, we exploit this capability and introduce PAM, a no-reference metric for assessing audio quality for different audio processing tasks.

Music Generation Text-to-Music Generation

NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription

no code implementations16 Jan 2024 Alon Vinnikov, Amir Ivry, Aviv Hurvitz, Igor Abramovski, Sharon Koubi, Ilya Gurvich, Shai Pe`er, Xiong Xiao, Benjamin Martinez Elizalde, Naoyuki Kanda, Xiaofei Wang, Shalev Shaer, Stav Yagev, Yossi Asher, Sunit Sivasankaran, Yifan Gong, Min Tang, Huaming Wang, Eyal Krupka

The challenge focuses on distant speaker diarization and automatic speech recognition (DASR) in far-field meeting scenarios, with single-channel and known-geometry multi-channel tracks, and serves as a launch platform for two new datasets: First, a benchmarking dataset of 315 meetings, averaging 6 minutes each, capturing a broad spectrum of real-world acoustic conditions and conversational dynamics.

Automatic Speech Recognition Benchmarking +4

Prompting Audios Using Acoustic Properties For Emotion Representation

no code implementations3 Oct 2023 Hira Dhamyal, Benjamin Elizalde, Soham Deshmukh, Huaming Wang, Bhiksha Raj, Rita Singh

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.

Contrastive Learning Retrieval +1

Pengi: An Audio Language Model for Audio Tasks

1 code implementation NeurIPS 2023 Soham Deshmukh, Benjamin Elizalde, Rita Singh, Huaming Wang

We introduce Pengi, a novel Audio Language Model that leverages Transfer Learning by framing all audio tasks as text-generation tasks.

Audio captioning Audio Question Answering +6

Real-Time Audio-Visual End-to-End Speech Enhancement

no code implementations13 Mar 2023 Zirun Zhu, Hemin Yang, Min Tang, ZiYi Yang, Sefik Emre Eskimez, Huaming Wang

In this paper, we propose a low-latency real-time audio-visual end-to-end enhancement (AV-E3Net) model based on the recently proposed end-to-end enhancement network (E3Net).

Speech Enhancement Task 2

Learning to mask: Towards generalized face forgery detection

no code implementations29 Dec 2022 Jianwei Fei, Yunshu Dai, Huaming Wang, Zhihua Xia

Our goal is to reduce the features that are easy to learn in the training phase, so as to reduce the risk of overfitting on specific forgery types.

Data Augmentation

Describing emotions with acoustic property prompts for speech emotion recognition

no code implementations14 Nov 2022 Hira Dhamyal, Benjamin Elizalde, Soham Deshmukh, Huaming Wang, Bhiksha Raj, Rita Singh

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.

Retrieval Speech Emotion Recognition

Real-Time Joint Personalized Speech Enhancement and Acoustic Echo Cancellation

no code implementations4 Nov 2022 Sefik Emre Eskimez, Takuya Yoshioka, Alex Ju, Min Tang, Tanel Parnamaa, Huaming Wang

Personalized speech enhancement (PSE) is a real-time SE approach utilizing a speaker embedding of a target person to remove background noise, reverberation, and interfering voices.

Acoustic echo cancellation Multi-Task Learning +1

Audio Retrieval with WavText5K and CLAP Training

1 code implementation28 Sep 2022 Soham Deshmukh, Benjamin Elizalde, Huaming Wang

In this work, we propose a new collection of web audio-text pairs and a new framework for retrieval.

AudioCaps Audio captioning +3

One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement

no code implementations20 Oct 2021 Hassan Taherian, Sefik Emre Eskimez, Takuya Yoshioka, Huaming Wang, Zhuo Chen, Xuedong Huang

Experimental results show that the proposed geometry agnostic model outperforms the model trained on a specific microphone array geometry in both speech quality and automatic speech recognition accuracy.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Personalized Speech Enhancement: New Models and Comprehensive Evaluation

no code implementations18 Oct 2021 Sefik Emre Eskimez, Takuya Yoshioka, Huaming Wang, Xiaofei Wang, Zhuo Chen, Xuedong Huang

Our results show that the proposed models can yield better speech recognition accuracy, speech intelligibility, and perceptual quality than the baseline models, and the multi-task training can alleviate the TSOS issue in addition to improving the speech recognition accuracy.

Speech Enhancement speech-recognition +1

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