Automatic Music Classification Research Articles

Retracted: Automatic Music Classification Model Based on Instantaneous Frequency and CNNs in High Noise Environment.

[This retracts the article DOI: 10.1155/2022/1317439.].

Influence of musical elements on the perception of ‘Chinese style’ in music

Recently Chinese music has been regarded as an independent music school. However, the definition of Chinese music has been an abstract and subjective concept. That makes music information retrieval (MIR) tasks hard to perform on Chinese music. Previous musicological studies have explained how musical elements like melody and instruments shape a certain kind of music genre including Chinese music, but the findings cannot be directly applied to relevant MIR tasks towards large-scale users and real-world problems. In this study, a pipeline of performing a perceptual survey is designed to explore how different musical elements influence people's perception of ‘Chinese style’ in music. Participants with various backgrounds were presented with categorised music excerpts performed in the Erhu or violin and then gave ‘Chinese style’ ratings. Statistical analysis indicates that music content contributes more than instruments. Results were compared between musicians and non-musicians. Subsequently, a supplementary automatic music classification experiment is conducted in comparison with the survey results to discuss the authors’ choice of stimuli in the survey and similarities between computer auditory and human perception. In general, the results in this study can be useful for MIR tasks, such as understanding, representation, and recommendation of Chinese music.

Automatic Music Classification Model Based on Instantaneous Frequency and CNNs in High Noise Environment.

Automatic music classification has significant research implications because it is the foundation for quick and efficient music resource retrieval and has a wide range of possible applications. In this study, DL is used to extract and categorize musical features, and a DL-based model for music feature extraction and classification is created. In this study, the instantaneous frequency and short-time Fourier transform are used to estimate the sine of a mixed music signal. Based on peak-frequency pairs, the DL algorithm is then used to calculate multiple candidate pitch estimates for each frame, and the melody pitch sequence is then obtained in accordance with the pitch profile duration and continuity characteristics. With this approach, the pitch can be calculated without reference to the fundamental frequency component. A music feature classification approach using spectrogram as input data and CNN as classifier is proposed at the same time in light of CNN's benefits in image processing. Studies reveal that this model's categorization and music feature extraction accuracy is as high as 94.18 percent and 95.66 percent, respectively. The outcomes demonstrate the efficiency of this technique for the extraction and classification of musical features. The field of music information retrieval is a good fit for it.

Dual-Function Integrated Emotion-Based Music Classification System Using Features From Physiological Signals

In this paper, we propose an emotion-based music classification system using features from physiological signals. The proposed system integrates two functions; the first uses physiological sensors to recognize the emotions of users listening to music, and the second classifies music according to the feelings evoked in the listeners, without using physiological sensors. Moreover, to directly predict the user’s emotions from sensor data acquired through wearable physiological sensors, we developed and implemented a hierarchical inner attention-mechanism-based deep neural network. To relieve the discomfort of users wearing physiological sensors every time to receive content recommendations, the relation between emotion-specific features that are extracted from previously generated physiological signals, and musical features that are extracted from music is learned through a regression neural network. Based on these models, the proposed system classifies input music automatically according to users’ emotional reactions without measuring human physiological signals. The experimental results not only demonstrate the accuracy of the proposed automatic music classification framework, but also provide a new perspective in which human experience-based characteristics related to emotion are applied to artificial-intelligence-based content classification.

Deep Neural Network for Musical Instrument Recognition Using MFCCs

The task of efficient automatic music classification is of vital importance and forms the basis for various advanced applications of AI in the musical domain. Musical instrument recognition is the task of instrument identification by virtue of its audio. This audio, also termed as the sound vibrations are leveraged by the model to match with the instrument classes. In this paper, we use an artificial neural network (ANN) model that was trained to perform classification on twenty different classes of musical instruments. Here we use use only the mel-frequency cepstral coefficients (MFCCs) of the audio data. Our proposed model trains on the full London philharmonic orchestra dataset which contains twenty classes of instruments belonging to the four families viz. woodwinds, brass, percussion, and strings. Based on experimental results our model achieves state-of-the-art accuracy on the same.

Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

With the arrival of the fourth industrial revolution, new technologies that integrate emotional intelligence into existing IoT applications are being studied. Of these technologies, emotional analysis research for providing various music services has received increasing attention in recent years. In this paper, we propose an emotion-based automatic music classification method to classify music with high accuracy according to the emotional range of people. In particular, when the new (unlearned) songs are added to a music-related IoT application, it is necessary to build mechanisms to classify them automatically based on the emotion of humans. This point is one of the practical issues for developing the applications. A survey for collecting emotional data is conducted based on the emotional model. In addition, music features are derived by discussing with the working group in a small and medium-sized enterprise. Emotion classification is carried out using multiple regression analysis and support vector machine. The experimental results show that the proposed method identifies most of induced emotions felt by music listeners and accordingly classifies music successfully. In addition, comparative analysis is performed with different classification algorithms, such as random forest, deep neural network and K-nearest neighbor, as well as support vector machine.

PRIMENA METODA MAŠINSKOG UČENJA ZA AUTOMATSKU KLASIFIKACIJU MUZIKE PO ŽANRU

Muzički žanrovi su konvencionalne kategorije koje se koriste za opisivanje muzike. Danas se najčešće koriste za klasifikaciju rastućeg broja muzičkih numera, koja bi dalje trebalo da omogući precizniju preporuku i jednostavniju pretragu muzike. U radu je analizirano nekoliko metoda i strategija za automatsku klasifikaciju muzike uključujući konvolucione neuronske mreže (Convolutional neural network – CNN), rekurente neuronske mreže (Reccurent neural network – RNN), mašine potpornih vektora (Support vecotor machines – SVM), random forrest (RF), AdaBoost kao i One vs. Rest (OVR) i klasifikaciju glasanjem. Muzičke numere klasifikovane su na osnovu mel-frequency cepstrum coefficients (MFCC) predstave audio zapisa, a za potrebe CNN-a korišćen je spektrogram. Ostvareni rezultati (~60%) se mogu porediti sa tačnošću (~70%) sa kojom su ljudi u stanju da ispravno procene muzički žanr kao i sa rezultatima ostvarenim u radovima koji su se bavili sličnom temom na istom skupu podataka. Obzirom da preciznost ostvarena u radu nije daleko od procene ljudi, metode bi mogle naći primenu u automatskoj klasifikaciji muzike za potrebe radio stanica ili web sajtova koji se bave distribuiranjem i preporukom muzičkih numera.

An Efficient Approach for Segmentation, Feature Extraction and Classification of Audio Signals

Due to the presence of non-stationarities and discontinuities in the audio signal, segmentation and classification of audio signal is a really challenging task. Automatic music classification and annotation is still considered as a challenging task due to the difficulty of extracting and selecting the optimal audio features. Hence, this paper proposes an efficient approach for segmentation, feature extraction and classification of audio signals. Enhanced Mel Frequency Cepstral Coefficient (EMFCC)-Enhanced Power Normalized Cepstral Coefficients (EPNCC) based feature extraction is applied for the extraction of features from the audio signal. Then, multi-level classification is done to classify the audio signal as a musical or non-musical signal. The proposed approach achieves better performance in terms of precision, Normalized Mutual Information (NMI), F-score and entropy. The PNN classifier shows high False Rejection Rate (FRR), False Acceptance Rate (FAR), Genuine Acceptance rate (GAR), sensitivity, specificity and accuracy with respect to the number of classes.

An integrated music video browsing system for personalized television

In this paper, we propose an integrated music video browsing system for personalized digital television. The system has the functions of automatic music emotion classification, automatic theme-based music classification, salient region detection, and shot classification. From audio (music) tracks, highlight detection and emotion classification are performed on the basis of information on temporal energy, timbre and tempo. For video tracks, shot detection is fulfilled to classify shots into face shots and color-based shots. Lastly automatic grouping of themes is executed on music titles and their lyrics. With a database of international music videos, we evaluate the performance of each function implemented in this paper. The experimental results show that the music browsing system achieves remarkable performances. Thus, our system can be adopted in any digital television for providing personalized services.

Automatic music classification and summarization

Automatic music classification and summarization are very useful to music indexing, content-based music retrieval and on-line music distribution, but it is a challenge to extract the most common and salient themes from unstructured raw music data. In this paper, we propose effective algorithms to automatically classify and summarize music content. Support vector machines are applied to classify music into pure music and vocal music by learning from training data. For pure music and vocal music, a number of features are extracted to characterize the music content, respectively. Based on calculated features, a clustering algorithm is applied to structure the music content. Finally, a music summary is created based on the clustering results and domain knowledge related to pure and vocal music. Support vector machine learning shows a better performance in music classification than traditional Euclidean distance methods and hidden Markov model methods. Listening tests are conducted to evaluate the quality of summarization. The experiments on different genres of pure and vocal music illustrate the results of summarization are significant and effective.