Experimenting with musically motivated convolutional neural networks

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

Untitled work

(2014) · Cited by 33,419 · see citing works

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works

Untitled work

(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works

Finding structure in time

Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212 · see citing works

Automatic early stopping using cross validation: quantifying the criteria

Prechelt, L. (1998) · Neural Networks · doi:10.1016/s0893-6080(98)00010-0 · Cited by 995 · see citing works

Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation

Huang, P.-S., Kim, M., Hasegawa‐Johnson, M., & Smaragdis, P. (2015) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2015.2468583 · Cited by 460 · see citing works

End-to-end learning for music audio

Dieleman, S., & Schrauwen, B. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854950 · Cited by 377 · see citing works

Improved musical onset detection with Convolutional Neural Networks

Schlüter, J., & Böck, S. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854953 · Cited by 195 · see citing works

A Simple Method to Determine if a Music Information Retrieval System is a “Horse”

Sturm, B. L. T. (2014) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2014.2330697 · Cited by 177 · see citing works

Exploring Data Augmentation for Improved Singing Voice Detection with Neural Networks.

Schlüter, J., & Grill, T. (2015) · doi:10.5072/zenodo.243314 · Cited by 174 · see citing works

Feature learning and deep architectures: new directions for music informatics

Humphrey, E. J., Bello, J. P., & LeCun, Y. (2013) · Journal of Intelligent Information Systems · doi:10.1007/s10844-013-0248-5 · Cited by 132 · see citing works

Evaluating Rhythmic descriptors for Musical Genre Classification

Dixon, S., Pampalk, E., & Widmer, G. (2004) · Cited by 117 · see citing works

Audio-Based Music Classification With A Pretrained Convolutional Network.

Dieleman, S., Brakel, P., & Schrauwen, B. (2011) · Ghent University Academic Bibliography (Ghent University) · doi:10.5281/zenodo.1415187 · Cited by 90 · see citing works

Multiscale Approaches To Music Audio Feature Learning.

Dieleman, S., & Schrauwen, B. (2013) · Ghent University Academic Bibliography (Ghent University) · doi:10.5281/zenodo.1416675 · Cited by 80 · see citing works

Chord Detection Using Deep Learning.

Zhou, X., & Lerch, A. (2015) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1416967 · Cited by 44 · see citing works

A Deep Bag-of-Features Model for Music Auto-TaggingPreprint

Nam, J., Herrera, J., & Lee, K. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1508.04999 · Cited by 14 · see citing works

The Modulation Scale Spectrum and its Application to Rhythm-Content Description.

Marchand, U., & Peeters, G. G. (2014) · Cited by 13 · see citing works

A closer look at deep learning neural networks with low-level spectral periodicity features

Sturm, B. L. T., Kereliuk, C., & Pikrakis, A. (2014) · International Workshop on Cognitive Information Processing (CIP) · doi:10.1109/cip.2014.6844511 · Cited by 10 · see citing works

Multi-Level and Multi-Scale Feature Aggregation Using Pretrained Convolutional Neural Networks for Music Auto-Tagging

Lee, J., & Nam, J. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2713830 · Cited by 148 · see citing works

Multimodal Deep Learning for Music Genre Classification

Oramas, S., Barbieri, F., Nieto, O., & Serra, X. (2018) · Transactions of the International Society for Music Information Retrieval · doi:10.5334/tismir.10 · Cited by 133 · see citing works

SampleCNN: End-to-End Deep Convolutional Neural Networks Using Very Small Filters for Music Classification

Lee, J., Park, J., Kim, K., & Nam, J. (2018) · Applied Sciences · doi:10.3390/app8010150 · Cited by 131 · see citing works

Timbre analysis of music audio signals with convolutional neural networks

Pons, J., Slizovskaia, O., Gong, R., Gómez, E., & Serra, X. (2017) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2017.8081710 · Cited by 129 · see citing works

Bottom-up broadcast neural network for music genre classification

Liu, C., Feng, L., Liu, G., Wang, H., & Liu, S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09643-6 · Cited by 122 · see citing works

End-to-end learning for music audio tagging at scalePreprint

Pons, J., Nieto, O., Prockup, M., Schmidt, E. M., Ehmann, A. F., & Serra, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02520 · Cited by 111 · see citing works

Sample-level Deep Convolutional Neural Networks for Music Auto-tagging Using Raw WaveformsPreprint

Lee, J., Park, J. Y., Kim, K. L., & Nam, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01789 · Cited by 107 · see citing works

A Deep Cybersickness Predictor Based on Brain Signal Analysis for Virtual Reality Contents

Kim, J., Kim, W., Oh, H., Lee, S., & Lee, S. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.01068 · Cited by 103 · see citing works

Comparison and Analysis of SampleCNN Architectures for Audio Classification

Kim, T., Lee, J., & Nam, J. (2019) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2019.2909479 · Cited by 89 · see citing works

mmASL

Santhalingam, P. S., Hosain, A. A., Zhang, D., Pathak, P. H., Rangwala, H., & Kushalnagar, R. (2020) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3381010 · Cited by 89 · see citing works

Deep-Learning-Based Multimodal Emotion Classification for Music Videos

Pandeya, Y. R., Bhattarai, B., & Lee, J. (2021) · Sensors · doi:10.3390/s21144927 · Cited by 86 · see citing works

Machine learning for music genre: multifaceted review and experimentation with audioset

Castillo, J. R., & Flores, M. J. (2019) · Journal of Intelligent Information Systems · doi:10.1007/s10844-019-00582-9 · Cited by 83 · see citing works

The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification

Koutini, K., Eghbal-zadeh, H., Dorfer, M., & Widmer, G. (2019) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco.2019.8902732 · Cited by 79 · see citing works

Multi-label Music Genre Classification from Audio, Text, and Images Using Deep FeaturesPreprint

Oramas, S., Nieto, O., Barbieri, F., & Serra, X. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.04916 · Cited by 71 · see citing works

Audio-based snore detection using deep neural networks

Xie, J., Aubert, X. L., Long, X., van Dijk, J., Arsenali, B., Fonseca, P. M., & Overeem, S. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105917 · Cited by 64 · see citing works

An Interpretable Deep Learning Model for Automatic Sound Classification

Zinemanas, P., Rocamora, M., Miron, M., Font, F., & Serra, X. (2021) · Electronics · doi:10.3390/electronics10070850 · Cited by 63 · see citing works

Parallel Recurrent Convolutional Neural Networks-Based Music Genre Classification Method for Mobile Devices

Yang, R., Feng, L., Wang, H., Yao, J., & Luo, S. (2020) · IEEE Access · doi:10.1109/access.2020.2968170 · Cited by 63 · see citing works

CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning

Stöter, F.-R., Chakrabarty, S., Edler, B., & Habets, E. A. P. (2018) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2018.2877892 · Cited by 62 · see citing works

Music Feature Maps with Convolutional Neural Networks for Music Genre Classification

Sénac, C., Pellegrini, T., Mouret, F., & Pinquier, J. (2017) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3095713.3095733 · Cited by 61 · see citing works

Designing efficient architectures for modeling temporal features with convolutional neural networks

Pons, J., & Serra, X. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952601 · Cited by 58 · see citing works

musicnn: Pre-trained convolutional neural networks for music audio tagging

Pons, J., & Serra, X. (2019) · arXiv · doi:10.48550/arxiv.1909.06654 · Cited by 67 · see citing works

Receptive Field Regularization Techniques for Audio Classification and Tagging With Deep Convolutional Neural Networks

Koutini, K., Eghbal-zadeh, H., & Widmer, G. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3082307 · Cited by 55 · see citing works

Masked Conditional Neural Networks for sound classification

Medhat, F., Chesmore, D., & Robinson, J. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106073 · Cited by 54 · see citing works

One deep music representation to rule them all? A comparative analysis of different representation learning strategies

Kim, J., Urbano, J., Liem, C. C. S., & Hanjalić, A. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04076-1 · Cited by 48 · see citing works

Representation Learning of Music Using Artist LabelsPreprint

Park, J. Y., Lee, J., Park, J., Ha, J.-W., & Nam, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.06648 · Cited by 45 · see citing works

Machine Friendly Machine Learning: Interpretation of Computed Tomography Without Image Reconstruction

Lee, H., Huang, C., Yune, S., Tajmir, S., Kim, M., & Do, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-51779-5 · Cited by 45 · see citing works

Deep Polyphonic ADSR Piano Note Transcription

Kelz, R., Böck, S., & Widmer, G. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683582 · Cited by 41 · see citing works

Predominant Instrument Recognition Based on Deep Neural Network With Auxiliary Classification

Yu, D., Duan, H., Fang, J., & Zeng, B. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2971419 · Cited by 41 · see citing works

Cochleogram-based approach for detecting perceived emotions in music

Russo, M., Kraljević, L., Stella, M., & Sikora, M. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102270 · Cited by 40 · see citing works

Comparing Shallow versus Deep Neural Network Architectures for Automatic Music Genre Classification.

Schindler, A., Lidy, T., & Rauber, A. (2016) · Cited by 37 · see citing works

Convolutional Neural Networks for Audio-Based Continuous Infant Cry Monitoring at Home

Xie, J., Long, X., Otte, R. A., & Shan, C. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3123906 · Cited by 34 · see citing works

Zero-shot Learning for Audio-based Music Classification and TaggingPreprint

Choi, J. D., Lee, J., Park, J. Y., & Nam, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.02670 · Cited by 33 · see citing works

Convolution-based classification of audio and symbolic representations of music

Velarde, G., Cancino-Chacón, C., Meredith, D., Weyde, T., & Grachten, M. (2018) · Journal of New Music Research · doi:10.1080/09298215.2018.1458885 · Cited by 32 · see citing works

Learning Hierarchy Aware Embedding from Raw Audio for Acoustic Scene Classification

Abrol, V., & Sharma, P. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.3001969 · Cited by 28 · see citing works

Frame-level Instrument Recognition by Timbre and PitchPreprint

Hung, Y.-N. A., & Yang, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.09587 · Cited by 27 · see citing works

Combining CNN and Broad Learning for Music Classification

Tang, H., & Chen, N. (2020) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2019edp7175 · Cited by 25 · see citing works

Discovering Speed Changes of Vehicles from Audio Data

Kubera, E., Wieczorkowska, A., Kuranc, A., & Słowik, T. (2019) · Sensors · doi:10.3390/s19143067 · Cited by 24 · see citing works

Deep Neural Network-Based Indoor Emergency Awareness Using Contextual Information From Sound, Human Activity, and Indoor Position on Mobile Device

Kim, H., & Kim, G. Y. (2020) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2020.3015197 · Cited by 23 · see citing works

Deep Learning Techniques for Koala Activity Detection

Himawan, I., Towsey, M., Law, B., & Roe, P. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1143 · Cited by 23 · see citing works

Learning Music Audio Representations Via Weak Language Supervision

Manco, I., Benetos, E., Quinton, E., & Fazekas, G. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746996 · Cited by 21 · see citing works

Music Genre Classification with Paralleling Recurrent Convolutional Neural NetworkPreprint

Feng, L., Liu, S., & Yao, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.08370 · Cited by 21 · see citing works

Music genre classification and recognition using convolutional neural network

Narkhede, N., Mathur, S., Bhaskar, A., & Kalla, M. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19243-3 · Cited by 20 · see citing works

Enhancing Audio Classification Through MFCC Feature Extraction and Data Augmentation with CNN and RNN Models

Rezaul, K. M., Jewel, M., Islam, M. S., Siddiquee, K. N. E. A., Barua, N., Rahman, M. A., Shan-A-Khuda, M., Sulaiman, R. B., Shaikh, M. S. I., Hamim, M. A., et al. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150704 · Cited by 20 · see citing works

Hybrid music recommendation with graph neural networks

Bevec, M., Tkalčič, M., & Pesek, M. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09410-4 · Cited by 20 · see citing works

Cross-Representation Transferability of Adversarial Attacks: From Spectrograms to Audio Waveforms

Koerich, K. M., Esmaeilpour, M., Abdoli, S., Britto, A. D. S., & Koerich, A. L. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207309 · Cited by 20 · see citing works

Masked Conditional Neural Networks for Audio Classification

Medhat, F., Chesmore, D., & Robinson, J. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68612-7_40 · Cited by 19 · see citing works

Automatic Classification of Music Genre Using Masked Conditional Neural Networks

Medhat, F., Chesmore, D., & Robinson, J. A. (2017) · Proceedings · doi:10.1109/icdm.2017.125 · Cited by 17 · see citing works

Multi-Task Music Representation Learning from Multi-Label Embeddings

Schindler, A., & Knees, P. (2019) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1109/cbmi.2019.8877462 · Cited by 17 · see citing works

Musical Tempo and Key Estimation using Convolutional Neural Networks with Directional FiltersPreprint

Schreiber, H., & Müller, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.10839 · Cited by 17 · see citing works

Masked Conditional Neural Networks for Environmental Sound Classification

Medhat, F., Chesmore, D., & Robinson, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-71078-5_2 · Cited by 16 · see citing works

Automatic Segmentation of Ethnomusicological Field Recordings

Marolt, M., Bohak, C., Kavčič, A., & Pesek, M. (2019) · Applied Sciences · doi:10.3390/app9030439 · Cited by 15 · see citing works

Automating Predictive Maintenance Using State-Based Transfer Learning and Ensemble Methods

Larocque-Villiers, J., Dumond, P., & Knox, D. (2021) · IEEE International Symposium on Robotic and Sensors Environments · doi:10.1109/rose52750.2021.9611768 · Cited by 15 · see citing works

Beyond Statistical Relations: Integrating Knowledge Relations into Style Correlations for Multi-Label Music Style Classification

Ma, Q., Yuan, C., Zhou, W. J., Han, J., & Hu, S. (2020) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3336191.3371838 · Cited by 15 · see citing works

Score-informed source separation of choral music

Gover, M., & Depalle, P. (2020) · doi:10.5281/zenodo.4245412 · Cited by 16 · see citing works

Acoustic Scene Classification from Binaural Signals using Convolutional Neural Networks

Mars, R., Pratik, P., Nagisetty, S., & Lim, C. (2019) · Detection and Classification of Acoustic Scenes and Events Workshop (DCASE) · doi:10.33682/6c9z-gd15 · Cited by 14 · see citing works

Music video emotion classification using slow–fast audio–video network and unsupervised feature representation

Pandeya, Y. R., Bhattarai, B., & Lee, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-98856-2 · Cited by 17 · see citing works

A Theory-Based Explainable Deep Learning Architecture for Music EmotionPreprint

Fong, H., Kumar, V., & Sudhir, K. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4025386 · Cited by 13 · see citing works

Genre Classification Empowered by Knowledge-Embedded Music Representation

Ding, H., Zhai, L., Zhao, C., Wang, F., Wang, G., Xi, W., Wang, Z., & Zhao, J. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3402115 · Cited by 11 · see citing works

Playing Technique Recognition by Joint Time–Frequency Scattering

Wang, C., Lostanlen, V., Benetos, E., & Chew, E. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053474 · Cited by 11 · see citing works

Randomly Weighted CNNs for (Music) Audio Classification

Pons, J., & Serra, X. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682912 · Cited by 10 · see citing works

Model-Based Deep Learning for Music Information Research: Leveraging diverse knowledge sources to enhance explainability, controllability, and resource efficiency

Gaël Richard, A., Lostanlen, V., Yang, Y., & Müller, M. (2024) · IEEE Signal Processing Magazine · doi:10.1109/msp.2024.3415569 · Cited by 10 · see citing works

Exploiting Heterogeneous Artist and Listener Preference Graph for Music Genre Classification

Yuan, C., Ma, Q., Chen, J., Zhou, W. J., Zhang, X., Tang, X., Han, J., & Hu, S. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414000 · Cited by 10 · see citing works

Hierarchical Classification for Instrument Activity Detection in Orchestral Music Recordings

Krause, M., & Müller, M. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3291506 · Cited by 10 · see citing works

Multi-temporal resolution convolutional neural networks for acoustic scene classification

Schindler, A., Lidy, T., & Rauber, A. (2018) · arXiv · doi:10.48550/arxiv.1811.04419 · Cited by 10 · see citing works

Deep Learning for Audio and Music

Peeters, G. G., & Gaël Richard, A. (2012) · Springer eBooks · doi:10.1007/978-3-030-74478-6_10 · Cited by 9 · see citing works

Audio Based Disambiguation Of Music Genre TagsPreprint

Hennequin, R., Royo-Letelier, J., & Moussallam, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.07256 · Cited by 9 · see citing works

Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video AnalysisPreprint

Schindler, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.00251 · Cited by 9 · see citing works

Robust handcrafted features for music genre classification

Muniz, V. H. D. S., & Filho, J. B. D. O. E. S. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08069-5 · Cited by 8 · see citing works

On the Automatic Identification of Music for Common Activities

Yadati, K., Liem, C. C. S., Larson, M., & Hanjalić, A. (2017) · International Conference on Multimedia Retrieval · doi:10.1145/3078971.3078997 · Cited by 8 · see citing works

Masked Conditional Neural Networks for Automatic Sound Events Recognition

Medhat, F., Chesmore, D., & Robinson, J. A. (2017) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2017.43 · Cited by 8 · see citing works

Content-based auto-tagging of audios using deep learning

Nayyar, R. K., Nair, S., Patil, O. R., Pawar, R., & Lolage, A. (2017) · International Conference on Big Data, IoT and Data Science (BID) · doi:10.1109/bid.2017.8336569 · Cited by 8 · see citing works

Acoustic Scene Classification Using Bilinear Pooling on Time-liked and Frequency-liked Convolution Neural Network

Kek, X. Y., Chin, C. S., & Li, Y. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9003150 · Cited by 8 · see citing works

A multi-branch convolutional neural network for snoring detection based on audio

Dong, H., Wu, H., Yang, G., Zhang, J., & Wan, K. (2024) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2024.2317438 · Cited by 8 · see citing works

Music Genre Classification Using Masked Conditional Neural Networks

Medhat, F., Chesmore, D., & Robinson, J. A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70096-0_49 · Cited by 8 · see citing works

Musician-AI partnership mediated by emotionally-aware smart musical instruments

Turchet, L., Stefani, D., & Pauwels, J. (2024) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103340 · Cited by 7 · see citing works

The Deep Learning Revolution in MIR: The Pros and Cons, the Needs and the Challenges

Peeters, G. G. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-70210-6_1 · Cited by 7 · see citing works

Multi-modal, Multi-task and Multi-label for Music Genre Classification and Emotion Regression

Pandeya, Y. R., You, J., Bhattarai, B., & Lee, J. (2021) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc52510.2021.9620826 · Cited by 7 · see citing works

Sound event localization and detection based on crnn using rectangular filters and channel rotation data augmentationPreprint

Ronchini, F., Arteaga, D., & Pérez-López, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.06422 · Cited by 7 · see citing works

Residual Attention Based Network for Automatic Classification of Phonation Modes

Sun, X., Jiang, Y. L., & Li, W. (2020) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme46284.2020.9102952 · Cited by 6 · see citing works

Ambiguity Modelling with Label Distribution Learning for Music Classification

Buisson, M., Alonso-Jiménez, P., & Bogdanov, D. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747467 · Cited by 6 · see citing works

POMET: a corpus for poetic meter classification

Rajan, R., Reghunath, L. C., & Varghese, L. T. (2022) · Language Resources and Evaluation · doi:10.1007/s10579-022-09604-5 · Cited by 6 · see citing works

Automatic Music Transcription with Convolutional Neural Networks using Intuitive Filter Shapes

Sleep, J. (2017) · doi:10.15368/theses.2017.95 · Cited by 6 · see citing works

Deformable CNN and Imbalance-Aware Feature Learning for Singing Technique Classification

Yamamoto, Y., Nam, J., & Terasawa, H. (2022) · Interspeech (USB) · doi:10.21437/interspeech.2022-11137 · Cited by 5 · see citing works

Environmental Sound Recognition Using Masked Conditional Neural Networks

Medhat, F., Chesmore, D., & Robinson, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69179-4_26 · Cited by 5 · see citing works

A sample-level DCNN for music auto-tagging

Yu, Y., Qi, M., Tang, Y., Deng, Q., Mai, F., & Zhaxi, N. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10330-9 · Cited by 4 · see citing works

On Generalist and Domain-Specific Music Classification Models and Their Impacts on Brazilian Music Genre Recognition

Silva, D. F., da Silva, A. C. M., Ortolan, L. F., & Marcacini, R. M. (2021) · Simpósio Brasileiro de Computação Musical (SBCM) · doi:10.5753/sbcm.2021.19427 · Cited by 3 · see citing works

Light-weight Frequency Information Aware Neural Network Architecture for Voice Spoofing Detection

Choi, S., Oh, S., Yang, J., Lee, Y., & Kwak, I. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956079 · Cited by 4 · see citing works

Knowledge-Graph Augmented Music Representation for Genre Classification

Ding, H., Song, W., Zhao, C., Wang, F., Wang, G., Xi, W., & Zhao, J. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10097131 · Cited by 4 · see citing works

Connecting the Last.fm Dataset to LyricWiki and MusicBrainz. Lyrics-based experiments in genre classification

Bodó, Z., & Szilágyi, E. (2018) · Acta Universitatis Sapientiae Informatica · doi:10.2478/ausi-2018-0009 · Cited by 4 · see citing works

Multimodal Fusion for Segment Classification in Folk Music

Krishnan, A., Vincent, A., Jos, G., & Rajan, R. (2021) · IEEE India Conference · doi:10.1109/indicon52576.2021.9691751 · Cited by 4 · see citing works

Application of Recurrent Neural Network for the Prediction of Target Non-Apneic Arousal Regions in Physiological Signals

Nesaragi, N., Majumder, S., Sharma, A., Tavakolian, K., & Patidar, S. (2018) · Computing in cardiology · doi:10.22489/cinc.2018.256 · Cited by 4 · see citing works

Receptive-Field Regularized CNNs for Music Classification and TaggingPreprint

Koutini, K., Eghbal-zadeh, H., Haunschmid, V., Primus, P., Chowdhury, S., & Widmer, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.13503 · Cited by 4 · see citing works

Ballroom Dance Recognition from Audio Recordings

Pavlín, T., Čech, J., & Matas, J. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412255 · Cited by 3 · see citing works

NAAQA: A Neural Architecture for Acoustic Question Answering

Abdelnour, J., Rouat, J., & Salvi, G. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3194311 · Cited by 3 · see citing works

Distance Metric Learnt Kernel-Based Music Classification Using Timbral Descriptors

Rajan, R., & S, S. M. B. (2021) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001421510149 · Cited by 3 · see citing works

From Visual to Acoustic Question AnsweringPreprint

Abdelnour, J., Salvi, G., & Rouat, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.11280 · Cited by 3 · see citing works

Frame-level Instrument Recognition by Timbre and PitchPreprint

Hung, Y.-N. A., & Yang, Y. (2018) · arXiv (Cornell University) · doi:10.5281/zenodo.1492362 · Cited by 3 · see citing works

InSE-NET: A Perceptually Coded Audio Quality Model based on CNNPreprint

Jiang, G., Biswas, A., Bergler, C., & Maier, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.13087 · Cited by 3 · see citing works

Music genre classification with modified residual learning and dual neural network

Ashraf, M., Abid, F., Raza, M. O., Rasheed, J., Alsubai, S., & Aşuroğlu, T. (2025) · PLoS ONE · doi:10.1371/journal.pone.0333808 · Cited by 2 · see citing works

Recognition of Acoustic Events Using Masked Conditional Neural Networks

Medhat, F., Chesmore, D., & Robinson, J. A. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.0-158 · Cited by 2 · see citing works

Designing efficient architectures for modeling temporal features with convolutional neural networks

Pons, J., & Serra, X. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952601 · Cited by 58 · see citing works

Deep convolutional networks on the pitch spiral for musical instrument recognitionPreprint

Lostanlen, V., & Cella, C.-E. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.06644 · Cited by 36 · see citing works

On the Robustness of Deep Convolutional Neural Networks for Music Classification.

Choi, K., Fazekas, G., Cho, K., & Sandler, M. B. (2017)

Representations of Sound in Deep Learning of Audio Features from MusicPreprint

Shuvaev, S. A., Giaffar, H., & Koulakov, A. A. (2017) · Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) · doi:10.48550/arxiv.1712.02898 · Cited by 5 · see citing works

An Enhanced Musical Instrument Classification using Deep Convolutional Neural Network

Prabavathy, S., Rathikarani, V., & Dhanalakshmi, P. (2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.d9271.118419 · Cited by 8 · see citing works