Marius Miron

Monoaural audio source separation using deep convolutional neural networks

In this paper we introduce a low-latency monaural source separation framework using a Convolutional Neural Network (CNN). We use a CNN to estimate time-frequency soft masks which are applied for source separation. We evaluate the performance of the neural network on a database comprising of musical mixtures of three instruments: voice, drums, bass as well as other instruments which vary from song to song. The proposed architecture is compared to a Multilayer Perceptron (MLP), achieving on-par results and a significant improvement in processing time. The algorithm was submitted to source separation evaluation campaigns to test efficiency, and achieved competitive results.

An open-source drum transcription system for Pure Data and Max MSP

This paper presents a drum transcription algorithm adjusted to the constraints of real-time audio. We introduce an instance filtering (IF) method using sub-band onset detection, which improves the performance of a system having at its core a feature-based K-nearest neighbor classifier (KNN). The architecture proposed allows for adapting different parts of the algorithm for either bass drum, snare drum or hi-hat cymbals. The open-source system is implemented in the graphic programming languages Pure Data (PD) and Max MSP, and aims to work with a large variety of drum sets. We evaluated its performance on a database of audio samples generated from a well known collection of midi drum loops randomly matched with a diverse collection of drum sets. Both of the evaluation stages, testing and validation, show an improvement in the performance when using the instance filtering algorithm.

Score-Informed Source Separation for Multichannel Orchestral Recordings

This paper proposes a system for score-informed audio source separation for multichannel orchestral recordings. The orchestral music repertoire relies on the existence of scores. Thus, a reliable separation requires a good alignment of the score with the audio of the performance. To that extent, automatic score alignment methods are reliable when allowing a tolerance window around the actual onset and offset. Moreover, several factors increase the difficulty of our task: a high reverberant image, large ensembles having rich polyphony, and a large variety of instruments recorded within a distant-microphone setup. To solve these problems, we design context-specific methods such as the refinement of score-following output in order to obtain a more precise alignment. Moreover, we extend a close-microphone separation framework to deal with the distant-microphone orchestral recordings. Then, we propose the first open evaluation dataset in this musical context, including annotations of the notes played by multiple instruments from an orchestral ensemble. The evaluation aims at analyzing the interactions of important parts of the separation framework on the quality of separation. Results show that we are able to align the original score with the audio of the performance and separate the sources corresponding to the instrument sections.

Immersive Orchestras: Audio Processing for Orchestral Music VR Content

This paper combines Audio Signal Processing and Virtual Reality (VR) content to create novel immersive experiences for orchestral music audiences. In VR, the auralization of sound sources of recorded live content remains still a rather unexplored topic. We aim to build a multimodal experience, where visual and audio cues bring a sonic augmentation of the real scene. In the particular scenario of orchestral music content, our goal is to acoustically zoom on a particular instrument when the VR user stares at it. This work aims to improve the learning aspects of music listening, either for education or for personal enrichment. We use audio signal processing to separate different sound sources (instruments) in a acoustic scene (orchestral music recording). Given the signals captured by multiple microphones and the musical score of the piece, our system is able to isolate the different instruments. From the processed separated tracks, we use a binaural rendering technique to emphasize a give instrument. For these experiments we used original content from top European orchestras.