Search Results for author:
Found 10 papers, 4 papers with code
CompA: Addressing the Gap in Compositional Reasoning in Audio-Language Models
In this paper, we propose CompA, a collection of two expert-annotated benchmarks with a majority of real-world audio samples, to evaluate compositional reasoning in ALMs.
Bridging High-Quality Audio and Video via Language for Sound Effects Retrieval from Visual Queries
We show that our system, trained using our automatic data curation pipeline, significantly outperforms baselines trained on in-the-wild data on the task of HQ SFX retrieval for video.
Efficient Spoken Language Recognition via Multilabel Classification
Spoken language recognition (SLR) is the task of automatically identifying the language present in a speech signal.
Language-Guided Audio-Visual Source Separation via Trimodal Consistency
We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data.
Music Enhancement via Image Translation and Vocoding
Consumer-grade music recordings such as those captured by mobile devices typically contain distortions in the form of background noise, reverb, and microphone-induced EQ.
Multimodal Metric Learning for Tag-based Music Retrieval
In this paper, we investigate three ideas to successfully introduce multimodal metric learning for tag-based music retrieval: elaborate triplet sampling, acoustic and cultural music information, and domain-specific word embeddings.
Mood Classification Using Listening Data
The mood of a song is a highly relevant feature for exploration and recommendation in large collections of music.
Predicting Audio Advertisement Quality
In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement.
End-to-end learning for music audio tagging at scale
The lack of data tends to limit the outcomes of deep learning research, particularly when dealing with end-to-end learning stacks processing raw data such as waveforms.
Sound Audio and Speech Processing
A Deep Multimodal Approach for Cold-start Music Recommendation
Second, track embeddings are learned from the audio signal and available feedback data.
