6 accepted papers
Music has a unique and complex structure which is challenging for both expert humans and existing AI systems to understand, and presents unique challenges relative to other forms of audio. We present LLark, an instruction-tuned multimodal model for *music* understanding. We detail our process for da…
Lyrics alignment gained considerable attention in recent years. State-of-the-art systems either re-use established speech recognition toolkits, or design end-to-end solutions involving a Connectionist Temporal Classification (CTC) loss. However, both approaches suffer from specific weaknesses: toolk…
Time-aligned lyrics can enrich the music listening experience by enabling karaoke, text-based song retrieval and intra-song navigation, and other applications. Compared to text-to-speech alignment, lyrics alignment remains highly challenging, despite many attempts to combine numerous sub-modules inc…
We define a novel system for the automatic estimation of downbeat positions from audio music signals. New rhythm and melodic features are introduced and feature adapted convolutional neural networks are used to take advantage of their specificity. Indeed, invariance to melody transposition, chroma d…
In the present work, we introduce a new probabilistic model for the task of estimating beat positions in a musical audio recording, instantiating the Conditional Random Field (CRF) framework. Our approach takes its strength from a sophisticated temporal modeling of the audio observations, accounting…
In this paper, we introduce a novel method for the automatic estimation of downbeat positions from music signals. Our system relies on the computation of musically inspired features capturing important aspects of music such as timbre, harmony, rhythmic patterns, or local similarities in both timbre…