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Audio Cover Song Identification using Convolutional Neural Network [article]

Sungkyun Chang, Juheon Lee, Sang Keun Choe, Kyogu Lee
2020 arXiv   pre-print
In this paper, we propose a new approach to cover song identification using a CNN (convolutional neural network).  ...  To do this, we first build the CNN using as an input a cross-similarity matrix generated from a pair of songs.  ...  All these findings motivated us to use the cross-similarity matrix with a convolutional neural network.  ... 
arXiv:1712.00166v2 fatcat:rdga2rsglnandptqbbexjl77za

Learning a Representation for Cover Song Identification Using Convolutional Neural Network [article]

Zhesong Yu, Xiaoshuo Xu, Xiaoou Chen, Deshun Yang
2019 arXiv   pre-print
We first train the network through classification strategies; the network is then used to extract music representation for cover song identification.  ...  In this paper, we propose a novel Convolutional Neural Network (CNN) architecture based on the characteristics of the cover song task.  ...  Moreover, deep learning approaches are introduced to cover song identification. For instance, CNNs are utilized to measure the similarity matrix [6] or learn features [7, 8, 9, 10] .  ... 
arXiv:1911.00334v1 fatcat:jfago6nx4fbihakq66746edrxq

Towards Cover Song Detection with Siamese Convolutional Neural Networks [article]

Marko Stamenovic
2020 arXiv   pre-print
Our results indicate that Siamese network configurations show promise for approaching the cover song identification problem.  ...  We train a neural architecture on tens of thousands of cover-song audio clips and test it on a held out set.  ...  Acknowledgements The author would like to thank Brian Lee for his insightful conversations, Colin Raffel for his help tracking down the Second Hand Songs data, the Recurse Center for providing a supportive  ... 
arXiv:2005.10294v1 fatcat:464lmdkooraknjfyldyikn4tn4

Emotion Detection Using Convolution Neural Network, Expert System and Deep Learning Approach

Prabha Seetaram Naik
2020 Bioscience Biotechnology Research Communications  
This work presents a facial expression identification system using the Facial Action Coding System with the use of the Bezier curves approximation method.  ...  For face feature identification, color segmentation is done with the help of fuzzy logic classification which minimizes color similarities.  ...  The face recognition is done using the Deep Neural Network technique and Convolution Neural Network. CNN are the neural network architecture which has multiple layers.  ... 
doi:10.21786/bbrc/13.13/34 fatcat:diizene265gopclxbrx6ydcgxm

A Prototypical Triplet Loss for Cover Detection [article]

Guillaume Doras, Geoffroy Peeters
2020 arXiv   pre-print
In a recent work, we addressed this problem with a convolutional neural network mapping each track's dominant melody to an embedding vector, and trained to minimize cover pairs distance in the embeddings  ...  We show that these changes improve results significantly for two concrete use cases, large dataset lookup and live songs identification.  ...  In a recent work, we proposed such a solution based on a convolutional neural network designed to map each track's dominant melody representation to a single embedding vector.  ... 
arXiv:1910.09862v2 fatcat:wzdiwtai2ffjvoqj4t6xkmllpu

Text-based Sentiment Analysis and Music Emotion Recognition [article]

Erion Çano
2018 arXiv   pre-print
First, deep neural networks need to be fed with data sets that are big in size as well as properly labeled.  ...  There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis.  ...  Prepackaged neural network architectures are very easy to use with little domain knowledge, producing top results.  ... 
arXiv:1810.03031v1 fatcat:4vj4euwtxbghbjdev2gutcgjny

Multimodal Deep Learning for Music Genre Classification

Sergio Oramas, Francesco Barbieri, Oriol Nieto, Xavier Serra
2018 Transactions of the International Society for Music Information Retrieval  
Music genre labels are useful to organize songs, albums, and artists into broader groups that share similar musical characteristics.  ...  Intermediate representations of deep neural networks are learned from audio tracks, text reviews, and cover art images, and further combined for classification.  ...  The content presented to the annotator was divided into 100 songs with audio tracks, 100 with cover images, and 100 with audio tracks and their corresponding cover images.  ... 
doi:10.5334/tismir.10 fatcat:xfkr3e3atne3hbiwoyaxqv35za

Automatic Recognition of Element Classes and Boundaries in the Birdsong with Variable Sequences

Takuya Koumura, Kazuo Okanoya, David S Vicario
2016 PLoS ONE  
As results, we demonstrated a hybrid model of a deep neural network and a hidden Markov model is effective in recognizing birdsong with variable note sequences.  ...  Our methods should be applicable, with small modification and tuning, to the songs in other species that hold the three properties of the sequential vocalization.  ...  Fig 4 . 4 Local classification with a deep convolutional neural network. Local classification was conducted with a deep convolutional neural network.  ... 
doi:10.1371/journal.pone.0159188 pmid:27442240 pmcid:PMC4956110 fatcat:7zutf2qin5ecvpjaghzq2b5jmi

Deep Image Features in Music Information Retrieval

Grzegorz Gwardys, Daniel Grzywczak
2014 International Journal of Electronics and Telecommunications  
Applications of Convolutional Neural Networks (CNNs) to various problems have been the subject of a number of recent studies ranging from image classification and object detection to scene parsing, segmentation  ...  CNNs are able to learn input data representation, instead of using fixed engineered features.  ...  Convolutional Neural Networks Convolutional Neural Networks (CNNs) are specific types of Artificial Neural Networks.  ... 
doi:10.2478/eletel-2014-0042 fatcat:s7vwo62lpvd5beadi2qo5qasly

Building a state space for song learning

Emily Lambert Mackevicius, Michale Sean Fee
2018 Current Opinion in Neurobiology  
I start with a neural network model for how premotor sequences may grow and split. This model predicts that the sequence-generating nucleus HVC would receive rhythmically patterned training inputs.  ...  Analysis of these datasets led us to develop a new method for unsupervised detection of neural sequences. Using this method, I was able to observe neural sequences even prior to tutor exposure.  ...  < 0 or (𝑡 + ℓ) > 𝑇 Tensor convolution operator Convolutive matrix factorization reconstructs a data matrix X using a 𝑁 × 𝐾 × 𝐿 tensor W and a 𝐾 × 𝑇 matrix H: ︀ X = W H = ∑︀ ℓ W ••ℓ ℓ→ H Note that  ... 
doi:10.1016/j.conb.2017.12.001 pmid:29268193 fatcat:3tzepkdwunhq5opwvounxwxvda

A fully convolutional deep auditory model for musical chord recognition

Filip Korzeniowski, Gerhard Widmer
2016 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP)  
In this paper, we present a chord recognition system that uses a fully convolutional deep auditory model for feature extraction.  ...  While these pipelines are traditionally hand-crafted, recent advances in end-to-end machine learning have begun to inspire researchers to explore data-driven methods for such tasks.  ...  Chords are a highly descriptive feature of music and useful e.g. for creating lead sheets for musicians or as part of higher-level tasks such as cover song identification.  ... 
doi:10.1109/mlsp.2016.7738895 dblp:conf/mlsp/KorzeniowskiW16 fatcat:g22nozkezzfwji24qkx3nygmae

Known Artist Live Song Id: A Hashprint Approach

T. J. Tsai, Thomas Prätzlich, Meinard Müller
2016 Zenodo  
on deep neural networks).  ...  Similar to the cover song detection problem, we would like to identify different performances of the same song.  ... 
doi:10.5281/zenodo.1418222 fatcat:xfo6s5mewzherpeimfmw4daezu

Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks [article]

Peter Li and Jiyuan Qian and Tian Wang
2015 arXiv   pre-print
In this paper, we present an application of convolutional neural networks for the task of automatic musical instrument identification.  ...  We show that a convolutional neural network trained on raw audio can achieve performance surpassing traditional methods that rely on hand-crafted features.  ...  Using convolutional neural networks, we train a model using raw audio as input.  ... 
arXiv:1511.05520v1 fatcat:g65s5u2ufbgpnacgyvlubz2b5i

Fast and accurate annotation of acoustic signals with deep neural networks

Elsa Steinfath, Adrian Palacios-Muñoz, Julian R Rottschäfer, Deniz Yuezak, Jan Clemens
2021 eLife  
DAS comes with a graphical user interface for annotating song, training the network, and for generating and proofreading annotations.  ...  DAS annotates song with high throughput and low latency for experimental interventions in realtime.  ...  Acknowledgements We thank Kurt Hammerschmidt for providing mouse data prior to publication. We thank Mala Murthy, David Stern, and  ... 
doi:10.7554/elife.68837 pmid:34723794 pmcid:PMC8560090 fatcat:glsyehz75ffx5nscyb7txts6qy

Exploring Robust Music Fingerprinting Methods With Data-driven Methodologies

Aditya Bhattacharjee, Marius Miron, Furkan Yesiler
2021 Zenodo  
Recent advancements in neural networks have shown that unsupervised and semi-supervised learning frameworks can be used to learn latent embeddings of musical data which, in turn, can be used as compact  ...  These frameworks are data-driven, scalable to real-world applications, and robust to noisy environments and transformations which may, advertently or inadvertently, make its way into the query audio record-ings  ...  Hence, let us bring our focus back to convolutional neural networks and translation invariance.  ... 
doi:10.5281/zenodo.5553864 fatcat:6x5lovub3fbqlkrq2asmig7bhm
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