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Timbre analysis of music audio signals with convolutional neural networks
- Jordi Pons, Olga Slizovskaia, Rong Gong, E. Gómez, X. Serra
- Computer Science25th European Signal Processing Conference…
- 20 March 2017
One of the main goals of this work is to design efficient CNN architectures — what reduces the risk of these models to over-fit, since CNNs' number of parameters is minimized.
Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks
- Eduardo Fonseca, Rong Gong, D. Bogdanov, Olga Slizovskaia, E. Gómez, X. Serra
- Computer ScienceDCASE
Comunicacio presentada al Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), celebrat el dia 16 de novembre de 2017 a Munic, Alemanya.
Towards an efficient deep learning model for musical onset detection
An efficient and reproducible deep learning model for musical onset detection (MOD) is proposed, which shows that the model pre-trained on one dataset fails to detect onsets on another dataset, which denotes the importance of providing the implementation code to enable re-training the model for a different dataset.
Real-time audio-to-score alignment of singing voice based on melody and lyric information
This paper aims at exploiting the advantages of melody and lyric information for real-time audio-to-score alignment of singing voice and suggests that lyric information can be efficiently used for any singer.
Automatic assessment of singing voice pronunciation: a case study with Jingju music
- Rong Gong
- Computer Science
- 23 November 2018
This dissertation aims to develop data-driven audio signal processing and machine learning (deep learning) models for automatic singing voice assessment in audio collections of jingju music, to make the current eurogeneric assessment approaches more culture- aware, and in return to develop new assessment approaches which can be generalized to other music traditions.
Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions
This paper tackles the singing voice phoneme segmentation problem in the singing training scenario by using language-independent information -- onset and prior coarse duration by using a duration-informed hidden Markov model (HMM).
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
Comunicacio presentada a: 15th Sound and Music Computing Conference (SMC2018). Sonic crossing, celebrat a Limassol, Xipre, del 4 al 7 de juliol de 2018.
ACOUSTIC SCENE CLASSIFICATION BY FUSING LIGHTGBM AND VGG-NET MULTICHANNEL PREDICTIONS
This report provides a solution for the task 1 of DCASE 2017 challenge by building two parallel audio scene classification systems – LightGBM and VGG-net and performing a linear logistic regression method to fuse the systems.
Pitch contour segmentation for computer-aided jinju singing training
Comunicacio presentada a: 13th Sound and Music Computing Conference (SMC 2016), celebrat a Hamburg (Alemanya), del 31 d'agost a 3 de setembre de 2016.
Comparison of the Singing Style of Two Jingju Schools
Comunicacio presentada a la 16th International Society for Music Information Retrieval Conference (ISMIR 2015), celebrada els dies 26 a 30 d'octubre de 2015 a Malaga, Espanya.