Add to ORKGIn computer vision, state-of-the-art object recognition sys-tems rely on label-preserving image transformations such as scaling and rotation to augment the training datasets. The additional training examples help the system to learn invariances that are difficult to build into the model, and improve generalization to unseen data. To the best of our knowledge, this approach has not been systematically ex-plored for music signals. Using the problem of singing voice detection with neural networks as an example, we ap-ply a range of label-preserving audio transformations to as-sess their utility for music data augmentation. In line with recent research in speech recognition, we find pitch shift-ing to be the most helpful augmentation method. Co...
In this paper, we introduce LA-Chorus, a chorus detection model based on latent feature augmentation...
Data augmentation has proven to be effective in training neural networks. Recently, a method called ...
The transcription of voice using neural networks is a technique that deserves attention, as speech a...
In computer vision, state-of-the-art object recognition sys-tems rely on label-preserving image tran...
Speech recognition in singing is a task that has not been widely researched so far. Singing possesse...
Singing voice detection is still a challenging task because the voice can be obscured by instruments...
Singing voice detection is still a challenging task because the voice can be obscured by instruments...
Singing voice detection is still a challenging task because the voice can be obscured by instruments...
Identifying musical instruments in a polyphonic music recording is a difficult yet crucial problem i...
Human voice recognition is a crucial task in music information retrieval. In this master thesis we d...
This thesis follows the trend of last decades in using neural networks in order to detect speech in ...
Automatic sung speech recognition is a challenging problem that remains largely unsolved. Challenges...
Supervised machine learning relies on the accessibility of large datasets of annotated data. This is...
A sung vocal line is the prominent feature of much popular music. It would be useful to locate the p...
The melody extraction problem is analogue to semantic segmentation on a time-frequency image, in whi...
In this paper, we introduce LA-Chorus, a chorus detection model based on latent feature augmentation...
Data augmentation has proven to be effective in training neural networks. Recently, a method called ...
The transcription of voice using neural networks is a technique that deserves attention, as speech a...
In computer vision, state-of-the-art object recognition sys-tems rely on label-preserving image tran...
Speech recognition in singing is a task that has not been widely researched so far. Singing possesse...
Singing voice detection is still a challenging task because the voice can be obscured by instruments...
Singing voice detection is still a challenging task because the voice can be obscured by instruments...
Singing voice detection is still a challenging task because the voice can be obscured by instruments...
Identifying musical instruments in a polyphonic music recording is a difficult yet crucial problem i...
Human voice recognition is a crucial task in music information retrieval. In this master thesis we d...
This thesis follows the trend of last decades in using neural networks in order to detect speech in ...
Automatic sung speech recognition is a challenging problem that remains largely unsolved. Challenges...
Supervised machine learning relies on the accessibility of large datasets of annotated data. This is...
A sung vocal line is the prominent feature of much popular music. It would be useful to locate the p...
The melody extraction problem is analogue to semantic segmentation on a time-frequency image, in whi...
In this paper, we introduce LA-Chorus, a chorus detection model based on latent feature augmentation...
Data augmentation has proven to be effective in training neural networks. Recently, a method called ...
The transcription of voice using neural networks is a technique that deserves attention, as speech a...