7 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…
Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task’s complexity, best results have typically been reported for systems focusing on specific settings, e.g. instrument-specific systems tend to yield improved results ov…
Deep learning-based approaches to musical source separation are often limited to the instrument classes that the models are trained on and do not generalize to separate unseen instruments. To address this, we propose a few-shot musical source separation paradigm. We condition a generic U-Net source…
The recent success of raw audio waveform synthesis models like WaveNet motivates a new approach for music synthesis, in which the entire process - creating audio samples from a score and instrument information - is modeled using generative neural networks. This paper describes a neural music synthes…
We present a novel time-domain pitch contour tracking algorithm based on Harmonic Locked Loops, which differs from existing method in terms of its approach, resolution, timbre information and speed. In addition to estimating pitch contours, the proposed method computes the amplitude of each harmonic…
In this paper we focus on the characterization of singing styles in world music.We develop a set of contour features capturing pitch structure and melodic embellishments.Using these features we train a binary classifier to distinguish vocal from non-vocal contours and learn a dictionary of singing s…
When recording a live musical performance, the different voices, such as the instrument groups or soloists of an orchestra, are typically recorded in the same room simultaneously, with at least one microphone assigned to each voice. However, it is difficult to acoustically shield the microphones. In…