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Found 14 papers, 6 papers with code
A Foundation Model for Music Informatics
This paper investigates foundation models tailored for music informatics, a domain currently challenged by the scarcity of labeled data and generalization issues.
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.
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.
Low-Resource Music Genre Classification with Cross-Modal Neural Model Reprogramming
In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR).
Feature-informed Embedding Space Regularization For Audio Classification
The workload is kept low during inference as the pre-trained features are only necessary for training.
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').
Feature-informed Latent Space Regularization for Music Source Separation
The integration of additional side information to improve music source separation has been investigated numerous times, e. g., by adding features to the input or by adding learning targets in a multi-task learning scenario.
AVASpeech-SMAD: A Strongly Labelled Speech and Music Activity Detection Dataset with Label Co-Occurrence
We propose a dataset, AVASpeech-SMAD, to assist speech and music activity detection research.
Transcription Is All You Need: Learning to Separate Musical Mixtures with Score as Supervision
Most music source separation systems require large collections of isolated sources for training, which can be difficult to obtain.
Multitask learning for instrument activation aware music source separation
Music source separation is a core task in music information retrieval which has seen a dramatic improvement in the past years.
Score-informed Networks for Music Performance Assessment
The assessment of music performances in most cases takes into account the underlying musical score being performed.
Musical Composition Style Transfer via Disentangled Timbre Representations
We investigate disentanglement techniques such as adversarial training to separate latent factors that are related to the musical content (pitch) of different parts of the piece, and that are related to the instrumentation (timbre) of the parts per short-time segment.
Audio and Speech Processing Sound
Hit Song Prediction for Pop Music by Siamese CNN with Ranking Loss
A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public.
