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Title: music classification: beyond supervised learning, towards real-world applications.

Abstract: Music classification is a music information retrieval (MIR) task to classify music items to labels such as genre, mood, and instruments. It is also closely related to other concepts such as music similarity and musical preference. In this tutorial, we put our focus on two directions - the recent training schemes beyond supervised learning and the successful application of music classification models. The target audience for this web book is researchers and practitioners who are interested in state-of-the-art music classification research and building real-world applications. We assume the audience is familiar with the basic machine learning concepts. In this book, we present three lectures as follows: 1. Music classification overview: Task definition, applications, existing approaches, datasets, 2. Beyond supervised learning: Semi- and self-supervised learning for music classification, 3. Towards real-world applications: Less-discussed, yet important research issues in practice.

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Music genre classification based on auditory image, spectral and acoustic features

Music genre classification using deep learning with knn.

Music genre is a conventional category that predicts the genre of music belonging to tradition or set of conventions. A music platform, with total assets of $26 billion, is ruling the music streaming stage today. At present, it has a huge number of tunes and it is information base and claims to have the right music score for everybody. Like, Spotify, Amazon music, Wynk has put a great deal in examination to further develop the manner in which clients find and pay attention to music. AI is at the centre of their examination. From NLP to Collaborative sifting to Deep Learning, All music platforms utilizes them all. Tunes are examined dependent on their advanced marks for certain elements, including rhythm, acoustics, energy, danceability, and so forth, to answer that incomprehensible old first-date inquiry. Organizations these days use music arrangement, either to have the option to put suggestions to their clients (like Spotify, Soundcloud) or just as an item (for instance, Shazam). Deciding music sorts is the initial phase toward that path. AI procedures have ended up being very fruitful in removing patterns and examples from a huge information pool. Similar standards are applied in Music Analysis moreover. Machine learning techniques are achieved in some recent years and rarely in deep learning. Most of the current music genre classification uses Machine learning techniques. In this, we present a music dataset which includes many genres like Rock, Pop, folk, Classical and many genres. A Deep learning approach is used in order to train and classify the system using KNN.

Music Genre Classification and Recommendation

There are various sorts to group the music. Classes are for the most part various classifications wherein music is partitioned. In this day and age as music industry develops quickly, there are various kinds of music sorts made. It is essential to classify the music into these classifications, yet it is mind boggling task. In past times this is done physically and prerequisite for programmed framework for type grouping emerges. As a rule, AI techniques are utilized to group music types and profound learning strategy is utilized to prepare the model yet in this undertaking, we will utilize neural organization strategies for the characterization.

Using Machine Learning to Classify Music Genre

Abstract: As Plato once rightfully said, ‘Music gives a soul to the universe, wings to the mind, flight to the imagination and life to everything.’ Music has always been an important art form, and more so in today’s science-driven world. Music genre classification paves the way for other applications such as music recommender models. Several approaches could be used to classify music genres. In this literature, we aimed to build a machine learning model to classify the genre of an input audio file using 8 machine learning algorithms and determine which algorithm is the best suitable for genre classification. We have obtained an accuracy of 91% using the XGBoost algorithm. Keywords: Machine Learning, Music Genre Classification, Decision Trees, K Nearest Neighbours, Logistic regression, Naïve Bayes, Neural Networks, Random Forest, Support Vector Machine, XGBoost

Music Genre Classification: A Comparative Study Between Deep Learning and Traditional Machine Learning Approaches

A novel music genre classification algorithm based on continuous wavelet transform and convolution neural network, multi-modal, multi-task and multi-label for music genre classification and emotion regression, distance metric learnt kernel-based music classification using timbral descriptors.

Automatic music genre classification based on distance metric learning (DML) is proposed in this paper. Three types of timbral descriptors, namely, mel-frequency cepstral coefficient (MFCC) features, modified group delay features (MODGDF) and low-level timbral feature sets are combined at the feature level. We experimented with k nearest neighbor (kNN) and support vector machine (SVM)-based classifiers for standard and DML kernels (DMLK) using GTZAN and Folk music dataset. Standard kernel-based kNN and SVM-based classifiers report classification accuracy (in%) of 79.03 and 90.16, respectively, on GTZAN dataset and 86.60 and 92.26, respectively, for Folk music dataset, with the best performing RBF kernel. A further improvement was observed when DML kernels were used in place of standard kernels in the kernel kNN and SVM-based classifiers with an accuracy of 84.46%, 92.74% (GTZAN), 90.00 and 96.23 (Folk music dataset) for DMLK-kNN and DMLK-SVM, respectively. The results demonstrate the potential of DML kernels in music genre classification task.

Music Genre Classification Using Acoustic Features and Autoencoders

Fast recognition using bar-based data mapping for gamelan music genre classification.

This research aims to develop a gamelan music genre classifier based on the musical mode system determined based on the dominant notes in a certain order. Only experts can discriminate the musical mode system of compositions. The Feed Forward Neural Networks method was used to classify gamelan compositions into three musical mode systems. The challenge is to recognize the musical mode system of compositions between the initial melody without having to analyze the entire melody using a small amount of data for the dataset. Instead of conducting a melodic extraction from audio signal data, the text-based skeletal melody data, which is a form of extracted melodic features, are used for the dataset. Unique corpuses are controlled based on the cardinality of the one-to-many relationship, and a data mapping technique based on the bars is used to increase the number of corpuses. The results show that the proposed method is suitable to solve the specified problems, where the accuracy in recognizing the class of unseen compositions between the initial melody achieves at 86.7%.

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Genre Classification in Music using Convolutional Neural Networks

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• First Online: 20 October 2023
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• Andrew Bawitlung 16 &
• Sandeep Kumar Dash 16  

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14322))

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• International Visual Informatics Conference

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With the advancement of technology and computational power, crafting a chart-topping song has become more effortless than before, achievable from the convenience of our residences with just a computer at hand. This has led to the emergence of vast arrays of catalogs of music, containing a variety of genres and styles from different music makers with different ethnicities and backgrounds, resulting in a large database that clogs most music streaming platforms with little automated categorization. Based on the GTZAN audio dataset, this paper revisits the use of Convolution Neural Networks (CNN) for classifying different types of music genres. Using Mel-frequency cepstral coefficients (MFCC) features, the CNN model achieved an accuracy of 85%. As a result of the careful design of the CNN model, it is on par with many latest and greatest CNN frameworks.

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National Institute of Technology, Mizoram, India

Andrew Bawitlung & Sandeep Kumar Dash

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Universiti Tenaga Nasional, Kajang, Selangor, Malaysia

Halimah Badioze Zaman

University of Cambridge, Cambridge, UK

Peter Robinson

Dublin City University, Dublin, Ireland

Alan F. Smeaton

MIT Sloan School of Management, Asia School of Business, Cambridge, MA, USA

Renato Lima De Oliveira

University of Southern Denmark, Odense, Denmark

Bo Nørregaard Jørgensen

National Central University, Jhongli, Taiwan

Timothy K. Shih

Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia

Rabiah Abdul Kadir

Ummul Hanan Mohamad

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Bawitlung, A., Dash, S.K. (2024). Genre Classification in Music using Convolutional Neural Networks. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2023. Lecture Notes in Computer Science, vol 14322. Springer, Singapore. https://doi.org/10.1007/978-981-99-7339-2_33

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Published : 20 October 2023

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Join the community, add a new evaluation result row, music genre classification.

21 papers with code • 1 benchmarks • 1 datasets

Benchmarks Add a Result

Most implemented papers, music genre classification with paralleling recurrent convolutional neural network.

Deep learning has been demonstrated its effectiveness and efficiency in music genre classification.

Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification

Here, we propose a new method that combines knowledge of human perception study in music genre classification and the neurophysiology of the auditory system.

Texture Selection for Automatic Music Genre Classification

julianofoleiss/lmd_3f_stratified_artist_filter • 28 May 2019

In this paper, we evaluate the impact of frame selection on automatic music genre classification in a bag of frames scenario.

Deep Learning Based EDM Subgenre Classification using Mel-Spectrogram and Tempogram Features

Along with the evolution of music technology, a large number of styles, or "subgenres," of Electronic Dance Music(EDM) have emerged in recent years.

Hierarchical quantum circuit representations for neural architecture search

matt-lourens/hierarqcal • 26 Oct 2022

The QCNN is a circuit model inspired by the architecture of Convolutional Neural Networks (CNNs).

Pre-training Music Classification Models via Music Source Separation

In this paper, we study whether music source separation can be used as a pre-training strategy for music representation learning, targeted at music classification tasks.

Lyrics-Based Music Genre Classification Using a Hierarchical Attention Network

In this study we apply recurrent neural network models to classify a large dataset of intact song lyrics.

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

Music genres allow to categorize musical items that share common characteristics.

Music Genre Classification using Masked Conditional Neural Networks

MCLNN has achieved accuracies that are competitive to state-of-the-art handcrafted attempts in addition to models based on Convolutional Neural Networks.

Bottom-up Broadcast Neural Network For Music Genre Classification

CaifengLiu/music-genre-classification • 24 Jan 2019

Music genre recognition based on visual representation has been successfully explored over the last years.

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This Is the Best Start to a Year We’ve Had in Pop This Decade (Essay)

By, like, a lot .

By Andrew Unterberger

Andrew Unterberger

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Around this time two years ago at Billboard , we were all asking: Where are the new hits ?

Through the first few months of 2022, the Billboard Hot 100 was stocked almost exclusively with holdovers from 2021 and even 2020 or earlier, with totally new music in precious short supply in the chart’s top tiers. Relief eventually came that month in the form of Harry Styles’ instant runaway smash “As It Was,” and then as April turned to May, via new albums by Future, Bad Bunny and Kendrick Lamar. But it still felt like the year was playing catch-up, like at midyear 2022 was still only just properly getting started.

J. Cole or Drake: Who Needs to Respond More to Kendrick Lamar's Verse? The Cases for Both

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Let’s start with the list of A-list artists who have already released entirely new albums by May 9: Beyoncé, Taylor Swift, Ariana Grande, Ye & Ty Dolla $ign, Future & Metro Boomin (twice!), J. Cole and Dua Lipa. (Depending on your “A-list” definition, you could also potentially throw Usher, Justin Timberlake and Kacey Musgraves on that list as well.) Hell, you could probably cut the list after the second name and the point would still stand: Any year where you get new sets by Beyoncé ( Cowboy Carter ) and Taylor Swift ( The Tortured Poets Department ) — the two most celebrated pop stars in the world right now — before Memorial Day, you’re probably off to a pretty fast start. And both sets have been enormous, world-building, culture-conquering affairs, with huge Hot 100-topping lead singles and no shortage of critical and fan discourse over their deeper implications.

Speaking of “Like That”: That Kendrick Lamar-assisted chart-topper essentially knocked the hip-hop world off its usual axis, kicking off the back-and-forth with Drake that has somehow managed to overshadow everything else that’s gone on in popular music so far this year. J. Cole responded first to Lamar’s pot-stirring “Like That” verse, on his lukewarmly received Might Delete Later mixtape and its closing “7 Minute Drill,” before publicly bowing out of the beef and deleting “Drill” from streaming services. But Drake was determined to get his money’s worth: He responded with both the leaked “Push Ups” and the social media-released “Taylor Made Freestyle” — which featured unlicensed, AI-generated guest verses “from” West Coast legends Snoop Dogg and the late Tupac Shakur, and was eventually taken down upon threat of legal action from the Shakur estate.

The Kendrick-Drake feud has been the biggest in music this year, but it wasn’t the first. The stage was set for that blockbuster beef by the January back-and-forth between Megan Thee Stallion, whose “Hiss” was thought to have subliminals aimed at rap rival Nicki Minaj (as well as additional lyrics assumed to be shots at Drake and other rap-world figures), and which inspired a response track (in addition to a lot of social media talk) from Minaj in the form of “Big Foot.” The fallout from that beef was mostly contained to the release week of the two tracks, but it helped Megan secure her first-ever entirely solo Hot 100 No. 1 for “Hiss,” and generally established the competitive tone for hip-hop among its biggest 2024 artists.

But the real reason 2024 has been so exciting, even beyond all these recognizable names showing up and showing out, is the equally impressive list of rising stars who have made their mark on the year so far.

Música Mexicana phenom Xavi began the year with two songs already climbing the top 100, and plenty more seemingly to come. Teddy Swims and Benson Boone have forced top 40 to make room for big soulful vocals and even bigger screaming guitar, with their crossover smashes “Lose Control” and “Beautiful Things,” respectively. Alt-rock has seen its fortunes revived on the chart through Djo’s psych-leaning “End of Beginning” and Artemas’ darkwave-inspired “I Like It When You Kiss Me,” both surprise top 20 Hot 100 hits. Even longtime cult favorite Hozier, a decade removed from his breakout hit “Take Me to Church,” is now back with a somehow-even-bigger hit: “Too Sweet,” lifted to No. 1 by good TikTok buzz and the currently rising tides of alt-folk and soul-pop.

For a few of these breakout artists, the success has been a long time coming. Sexyy Redd built up momentum for most of 2023 with viral hits “Pound Town” and “SkeeYee” — culminating in a feature appearance on Drake’s For All the Dogs No. 11 hit “Rich Baby Daddy” — but she’s taken it to a new level this year with her first solo top 20 hit, the dancefloor shout-along “Get It Sexyy.” Glorilla has taken a similar path to solo success with her own self-referencing smash “Yeah Glo!,” while also joining forces with Megan Thee Stallion for the chart-storming “Wanna Be.” Sabrina Carpenter and Chappell Roan were pop favorites with critical acclaim disproportionate to their actual top 40 presence — but following opening slots on Taylor Swift’s and Olivia Rodrigo’s recent tours, they’ve both seen raised profiles and higher levels of crossover stardom with new singles “Espresso,” and “Good Luck Babe!,” respectively, both all but sure to keep growing into the warm-weather months.

The sheer volume of impressive hits so far this year can be seen in the amount of turnover on the Hot 100 — particularly in the top spot, where no one song has reigned for more than three consecutive weeks (“Like That,” again). We’ve already seen 11 different songs top the Hot 100 across the first 19 chart weeks, compared to seven last year and just six in 2022. Both of those years saw a No. 1 hit reign for 15+ weeks seemingly almost by default: “As It Was” and Morgan Wallen’s “Last Night” didn’t dominate because they kept finding new ways to infiltrate pop culture (a la Lil Nas X with “Old Town Road” ), but simply because the competition usually just wasn’t strong enough across the board to consistently threaten their supremacy. This year, with everything that’s been happening, it seems unlikely that either song would even get to double-digit weeks on top.

Regardless of the reasons, it’s been a transfixing start to the year in popular music, with major contributions seemingly coming from all different corners of the music world, and from all different levels of artists. And what’s more, it doesn’t look to be slowing down anytime soon: This Friday brings with it a new album from Gunna and a new single from Post Malone and Morgan Wallen, the latter being arguably the biggest remaining recording artist in contemporary music who we haven’t heard much new from this year. And then the week after, it’s time for Billie Eilish’s much-hyped Hit Me Hard and Soft album, her first full-length set to arrive with no advance singles. Get your rest days in where you can and maybe hope for a bit of a summer vacation in a couple months, because it doesn’t look like pop is going to be taking it easy on us anytime in the near future — we’re exhausted, but elated.

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