Introduction
The field of music information retrieval and generation has witnessed significant advancements in recent times. Researchers have been exploring various approaches to improve the accuracy and efficiency of music-related tasks such as audio fingerprinting, beat tracking, and music generation.
General Direction
The current trend in the field is towards leveraging deep learning techniques and large datasets to achieve state-of-the-art results. Many researchers are focusing on developing novel architectures and training methods to improve the performance of music generation and retrieval models. Additionally, there is a growing interest in exploring the applications of music information retrieval and generation in real-world scenarios such as music production, recommendation systems, and music therapy.
Noteworthy Papers
- A paper on fine-tuning MIDI-to-audio alignment using a neural network on piano roll and CQT representations has shown promising results, achieving up to 20% higher alignment accuracy than the industry-standard Dynamic Time Warping method.
- Another paper on enhancing neural audio fingerprint robustness to audio degradation for music identification has proposed a series of best practices to enhance self-supervision by leveraging musical signal properties and realistic room acoustics, resulting in state-of-the-art performance on both synthetic and real-world datasets.