Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction

People often have trouble recognizing a song especially, if the song is sung by a not original artist which is called cover song. Hence, an identification system might be used to help recognize a song or to detect copyright violation. In this study, we try to recognize a song and a cover song by using the fingerprint of the song represented by features extracted from MPEG-7. The fingerprint of the song is represented by Audio Signature Type. Moreover, the fingerprint of the cover song is represented by Audio Spectrum Flatness and Audio Spectrum Projection. Furthermore, we propose a sliding algorithm and k-Nearest Neighbor (k-NN) with Bhattacharyya distance for song recognition and cover song recognition. The results of this experiment show that the proposed fingerprint technique has an accuracy of 100% for song recognition and an accuracy of 85.3% for cover song recognition.

Section: Song Recognition Methodsmentioning

confidence: 99%

“…MIR means processing the music spectrogram to obtain useful information [15] 1037 cover song detection is part of MIR. For example, music recommendation system using collaborative filtering and music genre classification also have been proposed [16], [17]. A fingerprint of song is characteristic for certain types of music.…”

Section: Introductionmentioning

confidence: 99%

Music fingerprinting based on bhattacharya distance for song and cover song recognition

Sarno¹,

Wijaya²,

Mahardika³

2019

“…The subsets is used for emotional classification with the dimensional model and convert it into categorical using Thayer"s model. 9 kinds of spectral shape audio extraction results can also be used as a feature of music emotion classification [19]. Roughness feature in audio spectrum is can be meant as spectral flux.…”

Section: Music Emotion Classification Based On Lyrics-audio Using Cbementioning

confidence: 99%

Music Emotion Classification based on Lyrics-Audio using Corpus based Emotion

Rachman

Sarno

Fatichah

2018

Music has lyrics and audio. That’s components can be a feature for music emotion classification. Lyric features were extracted from text data and audio features were extracted from audio signal data.In the classification of emotions, emotion corpus is required for lyrical feature extraction. Corpus Based Emotion (CBE) succeed to increase the value of F-Measure for emotion classification on text documents. The music document has an unstructured format compared with the article text document. So it requires good preprocessing and conversion process before classification process. We used MIREX Dataset for this research. Psycholinguistic and stylistic features were used as lyrics features. Psycholinguistic feature was a feature that related to the category of emotion. In this research, CBE used to support the extraction process of psycholinguistic feature. Stylistic features related with usage of unique words in the lyrics, e.g. ‘ooh’, ‘ah’, ‘yeah’, etc. Energy, temporal and spectrum features were extracted for audio features.The best test result for music emotion classification was the application of Random Forest methods for lyrics and audio features. The value of F-measure was 56.8%.

“…A similar algorithm, namely custom CNN, to classify images of female faces and male faces, was proposed by Zaman [3]. In addition, there is the k-nearest neighbor (KNN) and ID3 algorithm used in Sudarma and Harsemadi's research to classify music based on mood [4]. Next is naïve Bayes which is used to detect spam emails in a study conducted by Jaiswal et al [5].…”

Section: Introductionmentioning

confidence: 99%

Implementation of the C4.5 algorithm for micro, small, and medium enterprises classification

Lestari

Yulmaini

Aswin

et al. 2022

The coronavirus disease-19 (COVID-19) pandemic has spread to various countries including Indonesia. Thus, implementing large-scale social restrictions (Bahasa: Pembatasan Sosial Berskala Besar (PSBB)) has resulted in the paralysis of the economy in Indonesia. including micro, small, and medium enterprises (MSMEs) have decreased turnover and even went out of business. The Department of Cooperatives and Small and Medium Enterprises (SMEs) in Pesawaran Regency, Lampung, oversees 3,808 MSMEs, whose development should be monitored as a basis for determining policies. However, there are problems in classifying MSMEs according to their categories because they have to check the existing data one by one, so it takes a long time. Therefore, this study proposed the C4.5 algorithm to solve this problem. In addition, this research compared with the naïve Bayes algorithm to find out which algorithm had a good performance and is suitable for this case. The results showed that 91% of MSMEs were included in the micro category, 8% was in a small category, and 1% was in the medium category. Based on the results, it explained that the C4.5 algorithm was bigger than naïve Bayes with a difference in the value of 3.79%. It had an accuracy value of 99.2%. Meanwhile, naive Bayes was 95.41%.