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Found 12 papers, 0 papers with code
StemGen: A music generation model that listens
End-to-end generation of musical audio using deep learning techniques has seen an explosion of activity recently.
Scaling Up Music Information Retrieval Training with Semi-Supervised Learning
To our knowledge, this is the first attempt to study the effects of scaling up both model and training data for a variety of MIR tasks.
Multitrack Music Transcription with a Time-Frequency Perceiver
Multitrack music transcription aims to transcribe a music audio input into the musical notes of multiple instruments simultaneously.
SingNet: A Real-time Singing Voice Beat and Downbeat Tracking System
Singing voice beat and downbeat tracking posses several applications in automatic music production, analysis and manipulation.
Jointist: Simultaneous Improvement of Multi-instrument Transcription and Music Source Separation via Joint Training
Jointist consists of an instrument recognition module that conditions the other two modules: a transcription module that outputs instrument-specific piano rolls, and a source separation module that utilizes instrument information and transcription results.
Modeling the Rhythm from Lyrics for Melody Generation of Pop Song
Experiments for Chinese lyric-to-melody generation show that the proposed framework is able to model key characteristics of rhythm and pitch distributions in the dataset, and in a subjective evaluation, the melodies generated by our system were rated as similar to or better than those of a state-of-the-art alternative.
Binaural Rendering of Ambisonic Signals by Neural Networks
Binaural rendering of ambisonic signals is of broad interest to virtual reality and immersive media.
Jointist: Joint Learning for Multi-instrument Transcription and Its Applications
However, its novelty necessitates a new perspective on how to evaluate such a model.
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To catch a chorus, verse, intro, or anything else: Analyzing a song with structural functions
Conventional music structure analysis algorithms aim to divide a song into segments and to group them with abstract labels (e. g., 'A', 'B', and 'C').
Supervised Metric Learning for Music Structure Features
Music structure analysis (MSA) methods traditionally search for musically meaningful patterns in audio: homogeneity, repetition, novelty, and segment-length regularity.
Supervised Chorus Detection for Popular Music Using Convolutional Neural Network and Multi-task Learning
This paper presents a novel supervised approach to detecting the chorus segments in popular music.
Modeling the Compatibility of Stem Tracks to Generate Music Mashups
A music mashup combines audio elements from two or more songs to create a new work.
