Search Results for author:
Found 8 papers, 5 papers with code
Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search
We propose tempo translation functions that allow for efficient manipulation of tempo within a pre-existing embedding space whilst maintaining other properties such as genre.
Tempo estimation as fully self-supervised binary classification
This paper addresses the problem of global tempo estimation in musical audio.
On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations
Similarly, we show that the optimal selection of data augmentation strategies for contrastive learning of music audio embeddings is dependent on the downstream task, highlighting this as an important embedding design decision.
Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music
To model the periodicity of beats, state-of-the-art beat tracking systems use "post-processing trackers" (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo.
Tempo vs. Pitch: understanding self-supervised tempo estimation
Self-supervision methods learn representations by solving pretext tasks that do not require human-generated labels, alleviating the need for time-consuming annotations.
An Analysis Method for Metric-Level Switching in Beat Tracking
For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model.
Symbolic music generation conditioned on continuous-valued emotions
In addition, we provide a new large-scale dataset of symbolic music paired with emotion labels in terms of valence and arousal.
On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks
In this paper, we address a sub-topic of the broad domain of audio enhancement, namely musical audio bandwidth extension.
Ranked #1 on
Audio Super-Resolution
on DSD100
(using extra training data)
