scholarly journals KLASIFIKASI MUSIK MENGGUNAKAN POLYNOMIAL NEURAL NETWORK

2017 ◽  
Vol 3 (2) ◽  
pp. 94
Author(s):  
Prisca Pakan ◽  
Rocky Yefrenes Dillak
Keyword(s):  
Neural Network ◽  
Zero Crossing ◽  
Spectral Centroid ◽  
Polynomial Neural Network ◽  
Zero Crossing Rate

Penelitian ini bertujuan mengembangkan suatu metode yang dapat digunakan untuk melakukanklasifikasi terhadap jenis musik berdasarkan file audio dengan format wav menggunakan algoritmaRidge Polynomial Neural Network (RPNN). Pengklasifikasian file audio ke dalam suatu kelompokatau kelas, memerlukan ciri atau fitur dari file audio tersebut. Metode ekstrak fitur yang digunakanuntuk memperoleh ciri atau fitur dari file yang dimaksud adalah Spectral Centroid (SC), SortTime Energy (STE) dan Zero Crossing Rate (ZCR) yang diturunkan dalam domain waktu (timedomain) yang merupakan salah satu komponen data audio. Berdasarkan hasil dari penelitian inimenunjukkan bahwa pendekatan yang diusulkan mampu melakukan klasifikasi terhadap jenis musikberdasarkan file audio berformat wav dengan akurasi sebesar 90%

scholarly journals Penerapan Algoritma Levenberg-Marquadt dan Backpropagation Neural Network Untuk Klasifikasi Suara Manusia

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
David David
Keyword(s):  
Neural Network ◽  
Speech Processing ◽  
Backpropagation Neural Network ◽  
Zero Crossing ◽  
Spectral Centroid ◽  
Human Voice ◽  
Voice Signal ◽  
Zero Crossing Rate ◽  
Short Time ◽  
Short Time Energy

Abstract. Voice recognition technology is currently experiencing growth, especially in the case of speech processing. Speech processing is a way to extract the desired information from a voice signal. This study discusses the classification of human voice system male and female. Extract the characteristics of the voice signal in each frame time domain and frequency domain is to help simplify and speed calculations. The features for voice or other audio between Short Time Energy, Zero Crossing Rate, Spectral Centroid, and others. Test results show that the classification system the human voice using the backpropagation neural network and Levenberg-Marquadt algorithm to change matrix weight is very good because of the complexity and rapid calculation which is not too high. Database voice sample of 40 voices with the test data as much as 5 votes. The output of the system is the result of the classification that has been identified with a similarity value>=0.5 for male and <0.5 as a female. Testing using artificial neural network produced an average success rate in voice classification amounted to 91%.Keywords: Feature Extraction, Classification, Backpropagation, Levenberg-Marquadt Algorithm, Human Voice Abstrak. Teknologi pengenalan suara saat ini telah mengalami perkembangan terutama dalam hal speech processing. Speech processing merupakan suatu cara untuk mengekstrak informasi yang diinginkan dari sebuah sinyal suara. Penelitian ini membahas sistem klasifikasi suara manusia male dan female. Mengekstrak ciri dari sinyal suara setiap frame pada kawasan waktu dan kawasan frekuensi sangat membantu untuk  menyederhanakan dan mempercepat perhitungan. Adapun fitur-fitur untuk suara atau audio antara lain Short Time Energy, Zero Crossing Rate, Spectral Centroid dan lain-lain. Hasil pengujian sistem menunjukkan bahwa klasifikasi suara manusia dengan menggunakan jaringan saraf tiruan backpropagation dan algoritma Levenberg-Marquadt untuk perubahan matriks bobot, sangat baik dan cepat karena kompleksitas perhitungan yang tidak terlalu tinggi. Database sample suara sebanyak 40 buah dengan data test sebanyak 5 suara. Output dari sistem adalah hasil klasifikasi yang telah dikenali dengan nilai kemiripan >= 0,5 sebagai pria dan < 0,5 sebagai wanita. Pengujian dengan menggunakan jaringan saraf tiruan dihasilkan rata-rata tingkat keberhasilan dalam klasifikasi suara adalah sebesar 91 %.Kata Kunci: Feature Extraction, Klasifikasi, Backpropagation, Algoritma Levenberg-Marquadt, Suara Manusia

scholarly journals Analysis of Spectral Features for Speaker Clustering

2019 ◽  
Vol 8 (9S3) ◽  
pp. 134-137
Keyword(s):  
Speaker Identification ◽  
Spectral Feature ◽  
Feature Analysis ◽  
Spectral Features ◽  
Mean Square ◽  
Audio Signals ◽  
Speaker Clustering ◽  
Zero Crossing ◽  
Spectral Centroid ◽  
Zero Crossing Rate

In this paper Spectral feature like Spectral Roll off, Spectral Centroid, RMS (Root Mean Square) energy, Zero crossing Rate, Spectral irregularity, Brightness, of speech audio signals are extracted and analyzed. From analysis, prominent features are selected. These prominent features are used for speaker identification. For performing feature analysis, database of seven speakers is created. By using features, speakers are divided into two groups or clusters.

An accurate full-dimensional potential energy surface for the reaction OH + SO → H + SO2

2021 ◽  
Vol 23 (1) ◽  
pp. 487-497
Author(s):  
Jie Qin ◽  
Jun Li
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Invariant Polynomial ◽  
Network Approach ◽  
Neural Network Approach ◽  
Polynomial Neural Network

An accurate full-dimensional PES for the OH + SO ↔ H + SO2 reaction is developed by the permutation invariant polynomial-neural network approach.

scholarly journals Generalization of Patterns by Identification with Polynomial Neural Network

2010 ◽  
Vol 61 (2) ◽  
pp. 120-124 ◽  
Author(s):  
Ladislav Zjavka
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Functional Dependence ◽  
Polynomial Neural Network ◽  
New Type ◽  
Input Variables ◽  
The Brain ◽  
Functional Output ◽  
Simplified Form

Generalization of Patterns by Identification with Polynomial Neural Network Artificial neural networks (ANN) in general classify patterns according to their relationship, they are responding to related patterns with a similar output. Polynomial neural networks (PNN) are capable of organizing themselves in response to some features (relations) of the data. Polynomial neural network for dependence of variables identification (D-PNN) describes a functional dependence of input variables (not entire patterns). It approximates a hyper-surface of this function with multi-parametric particular polynomials forming its functional output as a generalization of input patterns. This new type of neural network is based on GMDH polynomial neural network and was designed by author. D-PNN operates in a way closer to the brain learning as the ANN does. The ANN is in principle a simplified form of the PNN, where the combinations of input variables are missing.

A Proposed Speaker Recognition Method B Based on Long-Term Voice Features and Fuzzy Logic

2021 ◽  
Vol 39 (1B) ◽  
pp. 1-10
Author(s):  
Iman H. Hadi ◽  
Alia K. Abdul-Hassan
Keyword(s):  
Fuzzy Logic ◽  
Speaker Recognition ◽  
Recognition Accuracy ◽  
Inner Product ◽  
Maximum Frequency ◽  
Recognition Method ◽  
Data Set ◽  
Zero Crossing ◽  
Zero Crossing Rate

Speaker recognition depends on specific predefined steps. The most important steps are feature extraction and features matching. In addition, the category of the speaker voice features has an impact on the recognition process. The proposed speaker recognition makes use of biometric (voice) attributes to recognize the identity of the speaker. The long-term features were used such that maximum frequency, pitch and zero crossing rate (ZCR).  In features matching step, the fuzzy inner product was used between feature vectors to compute the matching value between a claimed speaker voice utterance and test voice utterances. The experiments implemented using (ELSDSR) data set. These experiments showed that the recognition accuracy is 100% when using text dependent speaker recognition.

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