PENERAPAN JARINGAN SYARAF TIRUAN DENGAN RADIAL BASIS FUNCTION UNTUK PENGENALAN GENRE MUSIK

Kecerdasan           buatan        dapat        diaplikasikan dalam  banyak  bidang  dalam  kehidupan.  Penerapan kecerdasan buatan diantaranya dapat dicapai dengan pendekatan jaringan syaraf tiruan (JST). Salah satu contoh  metode  jaringan  syaraf  tiruan  yang  dikenal adalah        metode        radial       basis      function        (RBF). Jaringan  syaraf  tiruan  radial  basis  function  (JST RBF)   dikenal   sebagai   salah   satu   jaringan   syaraf yang  memiliki  tiga  lapis  bersifat  feedforward  yang dapat        memecahkan            masalah         klasifikasi          atau pengenalan   pola.   Dalam  penelitian   ini   JST   RBF digunakan   untuk   menglasifikasi   musik   ke   dalam genre       (jenis)       musik      berdasarkan          kedekatannya dengan target. Sebagai kebutuhan, jenis musik yang dipakai       pada      penelitian        ini     adalah      campursari, keroncong,  pop,  dan  rock  dengan  3  macam  durasi yaitu  2  detik,  5  detik,  dan  10  detik  pada  setiap musik.   Sedangkan   banyak   neuron   yang   dapakai dalam   lapisan   tersembunyi   sebanyak   56   neuron. Bahan masukan (input) yang digunakan dalam JST RBF ini berformat *.mp3 yang diunduh dari internet yang selanjutnya dikonversi ke dalam format *.wav dan diektraksi dengan  menggunakan  mel-frequency cepstrum          coeffisients           (MFCC).          Teknik         ini mengekstraksi  fitur  suara  yang  terdapat  pada  data musik.  Koefisien  yang  digunakan  dalam  penelitian ini  sebanyak  7  koefisien  untuk  setiap  data  musik. Dari  hasil  simulasi  program  menunjukkan  bahwa JST   RBF   dapat   mengklasifikasi                 musik   dengan akurasi   paling   tinggi   pada   data   uji   berdurasi   10 detik sebesar 75%. Kata  kunci  :  Genre,  jaringan  syaraf  tiruan, kecerdasan            buatan,          mel-frequency             cepstrum coefficients, musik, radial basis function

Masked Conditional Neural Networks for sound classification

The remarkable success of deep convolutional neural networks in image-related applications has led to their adoption also for sound processing. Typically the input is a time–frequency representation such as a spectrogram, and in some cases this is treated as a two-dimensional image. However, spectrogram properties are very different to those of natural images. Instead of an object occupying a contiguous region in a natural image, frequencies of a sound are scattered about the frequency axis of a spectrogram in a pattern unique to that particular sound. Applying conventional convolution neural networks has therefore required extensive hand-tuning, and presented the need to find an architecture better suited to the time–frequency properties of audio. We introduce the ConditionaL Neural Network (CLNN)1 and its extension, the Masked ConditionaL Neural Network (MCLNN) designed to exploit the nature of sound in a time–frequency representation. The CLNN is, broadly speaking, linear across frequencies but non-linear across time: it conditions its inference at a particular time based on preceding and succeeding time slices, and the MCLNN use a controlled systematic sparseness that embeds a filterbank-like behavior within the network. Additionally, the MCLNN automates the concurrent exploration of several feature combinations analogous to hand-crafting the optimum combination of features for a recognition task. We have applied the MCLNN to the problem of music genre classification, and environmental sound recognition on several music (Ballroom, GTZAN, ISMIR2004, and Homburg), and environmental sound (Urbansound8K, ESC-10, and ESC-50) datasets. The classification accuracy of the MCLNN surpasses neural networks based architectures including state-of-the-art Convolutional Neural Networks and several hand-crafted attempts

Sparse machine learning methods with applications in multivariate signal processing

This thesis details theoretical and empirical work that draws from two main subject areas: Machine Learning (ML) and Digital Signal Processing (DSP). A unified general framework is given for the application of sparse machine learning methods to multivariate signal processing. In particular, methods that enforce sparsity will be employed for reasons of computational efficiency, regularisation, and compressibility. The methods presented can be seen as modular building blocks that can be applied to a variety of applications. Application specific prior knowledge can be used in various ways, resulting in a flexible and powerful set of tools. The motivation for the methods is to be able to learn and generalise from a set of multivariate signals. In addition to testing on benchmark datasets, a series of empirical evaluations on real world datasets were carried out. These included: the classification of musical genre from polyphonic audio files; a study of how the sampling rate in a digital radar can be reduced through the use of Compressed Sensing (CS); analysis of human perception of different modulations of musical key from Electroencephalography (EEG) recordings; classification of genre of musical pieces to which a listener is attending from Magnetoencephalography (MEG) brain recordings. These applications demonstrate the efficacy of the framework and highlight interesting directions of future research

A Model for Predicting Music Popularity on Streaming Platforms

The global music market moves billions of dollars every year, most of which comes from streamingplatforms. In this paper, we present a model for predicting whether or not a song will appear in Spotify’s Top 50, a ranking of the 50 most popular songs in Spotify, which is one of today’s biggest streaming services. To make this prediction, we trained different classifiers with information from audio features from songs that appeared in this ranking between November 2018 and January 2019. When tested with data from June and July 2019, an SVM classifier with RBF kernel obtained accuracy, precision, and AUC above 80%