Recognizing Patterns of Music Signals to Songs Classification Using Modified AIS-Based Classifier

Human capabilities of recognizing different type of music and grouping them into categories of genre are so remarkable that experts in music can perform such classification using their hearing senses and logical judgment. For decades now, the scientific community were involved in research to automate the human process of recognizing genre of songs. These efforts would normally imitate the human method of recognizing the music by considering every essential component of the songs from artist voice, melody of the music through to the type of instruments used. As a result, various approaches or mechanisms are introduced and developed to automate the classification process. The results of these studies so far have been remarkable yet can still be improved. The aim of this research is to investigate Artificial Immune System (AIS) domain by focusing on the modified AIS-based classifier to solve this problem where the focuses are the censoring and monitoring modules. In this highlight, stages of music recognition are emphasized where feature extraction, feature selection, and feature classification processes are explained. Comparison of performances between proposed classifier and WEKA application is discussed

A Bio-Inspired Music Genre Classification Framework using Modified AIS-Based Classifier

For decades now, scientific community are involved in various works to automate the human process of recognizing different types of music using different elements for example different instruments used. These efforts would imitate the human method of recognizing the music by considering every essential component of the songs from artist voice, melody of the music through to the type of instruments used. Various approaches or mechanisms are introduced and developed to automate the classification process since then. The results of these studies so far have been remarkable yet can still be improved. The aim of this research is to investigate Artificial Immune System (AIS) domain by focusing on the modified AIS-based classifier to solve this problem where the focuses are the censoring and monitoring modules. In this highlight, stages of music recognition are emphasized where feature extraction, feature selection, and feature classification processes are explained. Comparison of performances between proposed classifier and WEKA application is discussed. Almost 20 to 30 percent of classification accuracies are increased in this study

Content-based feature selection for music genre classification

The most important aspect that one should consider in a content-based analysis study is the feature that represents the information. In music analysis one should know the details of the music contents that can be used to differentiate the songs. The selection of features to represent each music genre is an important step to identify, label, and classify the songs according to the genres. This research investigates, analyzes, and select timbre, rhythm, and pitch-based features to classify music genres. The features that were extracted from the songs consist the singer's voice, the instruments and the melody. The feature selection process focuses on the supervised and unsupervised methods with the reason to select significant generalized and specialized music features. Besides the selection process, two modules of Negative Selection Algorithm; censoring and monitoring are highlighted as well in this work. We then proposed the Modified AIS-based classification algorithm to solve the music genre classification problem. The results from our experiments demonstrate that the features selection process contributes to the proposed modified AIS-based music genre classification performs significantly in classifying the music genres

The GTZAN dataset: Its contents, its faults, their effects on evaluation, and its future use

The GTZAN dataset appears in at least 100 published works, and is the most-used public dataset for evaluation in machine listening research for music genre recognition (MGR). Our recent work, however, shows GTZAN has several faults (repetitions, mislabelings, and distortions), which challenge the interpretability of any result derived using it. In this article, we disprove the claims that all MGR systems are affected in the same ways by these faults, and that the performances of MGR systems in GTZAN are still meaningfully comparable since they all face the same faults. We identify and analyze the contents of GTZAN, and provide a catalog of its faults. We review how GTZAN has been used in MGR research, and find few indications that its faults have been known and considered. Finally, we rigorously study the effects of its faults on evaluating five different MGR systems. The lesson is not to banish GTZAN, but to use it with consideration of its contents.Comment: 29 pages, 7 figures, 6 tables, 128 reference

Classifying music perception and imagination using EEG

This study explored whether we could accurately classify perceived and imagined musical stimuli from EEG data. Successful EEG-based classification of what an individual is imagining could pave the way for novel communication techniques, such as brain-computer interfaces. We recorded EEG with a 64-channel BioSemi system while participants heard or imagined different musical stimuli. Using principal components analysis, we identified components common to both the perception and imagination conditions however, the time courses of the components did not allow for stimuli classification. We then applied deep learning techniques using a convolutional neural network. This technique enabled us to classify perception of music with a statistically significant accuracy of 28.7%, but we were unable to classify imagination of music (accuracy = 7.41%). Future studies should aim to determine which characteristics of music are driving perception classification rates, and to capitalize on these characteristics to raise imagination classification rates

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

INSAM Journal of Contemporary Music, Art and Technology 2

The subject of machine learning and creativity, as well as its appropriation in arts is the focus of this issue with our Main theme of – Artificial Intelligence in Music, Arts, and Theory. In our invitation to collaborators, we discussed our standing preoccupation with the exploration of technology in contemporary theory and artistic practice. The invitation also noted that this time we are encouraged and inspired by Catherine Malabou’s new observations regarding brain plasticity and the metamorphosis of (natural and artificial) intelligence. Revising her previous stance that the difference between brain plasticity and computational architecture is not authentic and grounded, Malabou admits in her new book, Métamorphoses de l'intelligence: Que faire de leur cerveau bleu? (2017), that plasticity – the potential of neuron architecture to be shaped by environment, habits, and education – can also be a feature of artificial intelligence. “The future of artificial intelligence,” she writes, “is biological.” We wanted to provoke a debate about what machines can learn and what we can learn from them, especially regarding contemporary art practices. On this note, I am happy to see that our proposition has provoked intriguing and unique responses from various different disciplines including: theory of art, aesthetics of music, musicology, and media studies. The pieces in the (Inter)view section deal with machine and computational creativity, as well as the some of the principles of contemporary art. Reviews give us an insight into a couple of relevant reading points for this discussion and a retrospective of one engaging festival that also fits this theme

The avian dawn chorus across Great Britain: using new technology to study breeding bird song

The avian dawn chorus is a period of high song output performed daily around sunrise during the breeding season. Singing at dawn is of such significance to birds that they remain motivated to do so amid the noise of numerous others. Yet, we still do not fully understand why the dawn chorus exists. Technological advances in recording equipment, data storage and sound analysis tools now enable collection and scrutiny of large acoustic datasets, encouraging research on sound-producing organisms and promoting ‘the soundscape’ as an indicator of ecosystem health. Using an unrivalled dataset of dawn chorus recordings collected during this thesis, I explore the chorus throughout Great Britain with the prospect of furthering our understanding and appreciation of this daily event. I first evaluate the performance of four automated signal recognition tools (‘recognisers’) when identifying the singing events of target species during the dawn chorus, and devise a new ensemble approach that improves detection of singing events significantly over each of the recognisers in isolation. I then examine daily variation in the timing and peak of the chorus across the country in response to minimum overnight temperature. I conclude that cooler temperatures result in later chorus onset and peak the following dawn, but that the magnitude of this effect is greater at higher latitude sites with cooler and less variable overnight temperature regimes. Next, I present evidence of competition for acoustic space during the dawn chorus between migratory and resident species possessing similar song traits, and infer that this may lead either to fine-scale temporal partitioning of song, such that each competitor maintains optimal output, or to one competitor yielding. Finally, I investigate day-to-day attenuation of song during the leaf-out period from budburst through to full-leaf in woodland trees, and establish the potential for climate-driven advances in leaf-out phenology to attenuate song if seasonal singing activity in birds has not advanced to the same degree. I find that gradual attenuation of sound through the leaf-out process is dependent on the height of the receiver, and surmise that current advances in leaf-out phenology are unlikely to have undue effect on song propagation. This project illustrates the advantage of applying new technology to ecological studies of complex acoustic environments, and highlights areas in need of improvement, which is essential if we are to comprehend and preserve our natural soundscapes

Acoustic Sensing: Mobile Applications and Frameworks

Acoustic sensing has attracted significant attention from both academia and industry due to its ubiquity. Since smartphones and many IoT devices are already equipped with microphones and speakers, it requires nearly zero additional deployment cost. Acoustic sensing is also versatile. For example, it can detect obstacles for distracted pedestrians (BumpAlert), remember indoor locations through recorded echoes (EchoTag), and also understand the touch force applied to mobile devices (ForcePhone). In this dissertation, we first propose three acoustic sensing applications, BumpAlert, EchoTag, and ForcePhone, and then introduce a cross-platform sensing framework called LibAS. LibAS is designed to facilitate the development of acoustic sensing applications. For example, LibAS can let developers prototype and validate their sensing ideas and apps on commercial devices without the detailed knowledge of platform-dependent programming. LibAS is shown to require less than 30 lines of code in Matlab to implement the prototype of ForcePhone on Android/iOS/Tizen/Linux devices.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143971/1/yctung_1.pd