Musical Instrument Recognition Research Articles

Piano Education of Children Using Musical Instrument Recognition and Deep Learning Technologies Under the Educational Psychology.

The objective of the study was to enhance quality education in the traditional pre-school piano education. Deep Learning (DL) technology is applied to piano education of children to improve their interest in learning music. Firstly, the problems of the traditional piano education of children were analyzed with the teaching patterns discussed under educational psychology, and a targeted music education plan was established. Secondly, musical instrument recognition technology was introduced, and the musical instrument recognition model was implemented based on DL. Thirdly, the proposed model was applied to the piano education of children to guide the music learning of students and improve their interest in piano learning. The feature recognition and acquisition of the proposed model were improved. Finally, the different teaching patterns were comparatively analyzed through the Questionnaire Survey (QS). The experimental results showed that the instrument recognition accuracy of Hybrid Neural Network (HNN) is 97.2%, and with the increase of iterations, the recognition error rate of the model decreases and stabilizes. Therefore, the proposed HNN based on DL for musical instrument recognition can accurately identify musical features. The QS results showed that the introduction of musical instrument recognition technology in the piano education of children can improve their interest in piano learning. Therefore, the establishment of the piano education patterns based on the piano education model can improve the effectiveness of teaching piano to students. This research provides a reference for the intelligentization of children's piano education.

Emotion recognition of musical instruments based on convolution long short time memory depth neural network

In this paper, a method of emotion recognition for musical instruments based on convolution long short time memory depth neural network is proposed, and an emotion recognition music database composed of four musical instruments is established, including keyboard instruments, wind instruments, string instruments and percussion instruments. The emotional types of these four instruments are divided into happiness, anger, sadness and fear. Through the establishment of CLDNN model based musical instrument emotion recognition architecture, MFCCs, CNN and CFS are used for feature extraction and training. The experimental structure shows that the best classification effect is to use long-term memory (LSTM) and deep neural network (DNN) to extract and combine the emotional feature sets of musical instruments, which has the highest accuracy. Considering the dynamic changes of musical features in musical instruments, the modeling method is used to predict the emotional changes of musical instruments.

Recognition and Classification Model of Music Genres and Chinese Traditional Musical Instruments Based on Deep Neural Networks

The teaching of ideological and political theory courses and daily ideological and political education are two important parts of education for college students. With the iterative update of information technology, the individualized development of students, and the reform and innovation of ideological and political education, higher goals and requirements have been put forward for ideological and political education. Some universities have developed new paths in the teaching model, but they have not considered the evaluation module and paid little attention to their own development. They only paid attention to the fact that it injected fresh blood into the reform of education model and ideological education but ignored the improvement of their own quality. Therefore, with these limitations, the learning effect is not satisfactory. Keeping in view these issues, this article defines the concept of deep learning and ideological and political education of college students as the starting point and then analyzes the new precise and personalized concepts, new forms of intelligent teaching and evaluation, and new models of intelligent learning that deep learning brings to college students’ ideological and political education. This is a new path of intelligent linkage with the subject, object, and mediator. It can deepen the reform of the education and teaching mode of individualization, accuracy, interactivity, and vividness of college students’ ideological and political education and improve the evaluation and management of college students’ ideological and political education. The experimental results of the study showed the effectiveness of the proposed study.

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.

RETRACTED ARTICLE: Music singing video teaching and timbre automation analysis based on dynamic programming and surveillance video summary algorithm

Music is an indispensable artistic way to cultivate people's sentiments in people's lives. Recognizing the timbre of music can be done in an intelligent way through recognition technology. In musical instrument recognition, the timbre of the musical instrument needs to be distinguished. Under the conditions of modern technology, dynamic programming algorithms are used to design the data information in the system. If the time efficiency of the algorithm is not enough, the time efficiency of the dynamic programming algorithm is limited. Will produce optimization. The solution of the problem of dynamic programming algorithm requires the design of different models for experimental analysis. In this paper, the time–frequency function, cepstrum function, sparse attribute and probability are analyzed experimentally, which can confirm that the time–frequency domain of the timbre in the instrument can correctly identify the instrument. The frequency spectrum of deep learning time–frequency information can extract high-frequency situations and use it to identify musical instruments. The input of CNN can have a significant effect on abstract features, and the auditory spectrogram masters the time–frequency information of music. Under the same network structure, the accuracy of MFCC and spectrogram has been significantly improved. Because CNN has a weight distribution feature, the increase in the input size of the convolutional network will not significantly increase the training time, thereby greatly reducing the complexity of training.

Modeling the onset advantage in musical instrument recognition.

Sound onsets provide particularly valuable cues for musical instrument identification by human listeners. It has remained unclear whether this onset advantage is due to enhanced perceptual encoding or the richness of acoustical information during onsets. Here this issue was approached by modeling a recent study on instrument identification from tone excerpts [Siedenburg. (2019). J. Acoust. Soc. Am. 145(2), 1078-1087]. A simple Hidden Markov Model classifier with separable Gabor filterbank features simulated human performance and replicated the onset advantage observed previously for human listeners. These results provide evidence that the onset advantage may be driven by the distinct acoustic qualities of onsets.

The influence of sampling frequency on tone recognition of musical instruments

Sampling frequency of musical instruments tone recognition generally follows the Shannon sampling theorem. This paper explores the influence of sampling frequency that does not follow the Shannon sampling theorem, in the tone recognition system using segment averaging for feature extraction and template matching for classification. The musical instruments we used were bellyra, flute, and pianica, where each of them represented a musical instrument that had one, a few, and many significant local peaks in the Discrete Fourier Transform (DFT) domain. Based on our experiments, until the sampling frequency is as low as 312 Hz, recognition rate performance of bellyra and flute tones were influenced a little since it reduced in the range of 5%. However, recognition rate performance of pianica tones was not influenced by that sampling frequency. Therefore, if that kind of reduced recognition rate could be accepted, the sampling frequency as low as 312 Hz could be used for tone recognition of musical instruments.

Music instrument recognition using deep convolutional neural networks

Musical instruments identification in polyphonic is a challenge in music information retrieval. In proposed work, a deep convolution neural network framework for predominant instrument recognition in real-world polyphonic music is accomplished. The network is trained on fixed-length music with a labeled predominant instrument and estimate an arbitrary number of instruments from an audio signal with variable length. The Mel spectrogram representation is used to map audio data into the matrix format. This work used eight layer convolution neural network for instrument recognition. ReLu activation function is used for the scaling of training data and introduces non-linearity in the network. At each layer, Max Pooling function is used for the dimension reduction. For the regularization, dropout is used which prevent the output from getting overfitting. The Softmax function gives the probability of particular instruments. The research excellent result with 92.8% accuracy.

Signal discrimination using category-preserving bag-of-words model for condition monitoring

Signal discrimination contributes to the development of machine–machine and human–machine interactive intelligent systems. In this study, a novel framework for signal discrimination was proposed. The proposed framework comprised three phases. In Phase I, a waveform shape-based feature extraction method was used for parameterizing signals. In Phase II, a novel category-preserving bag-of-words (CPBoW) model was proposed. In Phase III, signals were discriminated using a vector space model with term frequency–inverse document frequency. The bag-of-words model generally demonstrated promising performance for signal discrimination. However, the inherent connections among signals of homogeneous categories were considerably lost during signal framing and codebook generation processes. This was because the codebook was simply generated by clustering signal frame samples in the Euclidean space. In the proposed CPBoW model, Taguchi’s quality engineering method was used to develop a category-preserving distance metric for executing a clustering process to generate category-preserving codewords. This preserved category information in the codebook and consequently increased the effectiveness of the discrimination process. The proposed framework was verified through three condition monitoring applications that involved a musical instrument recognition problem, motor bearing fault recognition problem, and heart disease recognition problem. The results indicated the superior performance and effectiveness of the proposed framework.

Musical Instrument Recognition using Mel-Frequency Cepstral Coefficients and Learning Vector Quantization

Musical instrument recognition is an essential subtask in many application regarding in music information retrieval. This research aims at extending the previous research saying that the MFCC used to feature extraction and LVQ method used to classification has good accuracy for musical instrument recognition. To test the methods described have been implemented in an android based application. Looking at the presented results, this research then focuses on to implementation that method for recognition musical instrument based on Aerophone, Electrophone, Chordophone, Idiophone, and Membranophone. The test was performed used 750 dataset with 5 sound source classes, the result of the performance test show that methods has 94.80% accuracy. It can be concluded that MFCC and LVQ methods can be implemented to recognize musical instruments.

Perceptually salient spectrotemporal modulations for recognition of sustained musical instruments.

Modulation Power Spectra include dimensions of spectral and temporal modulation that contribute significantly to the perception of musical instrument timbres. Nevertheless, it remains unknown whether each instrument's identity is characterized by specific regions in this representation. A recognition task was applied to tuba, trombone, cello, saxophone, and clarinet sounds resynthesized with filtered spectrotemporal modulations. The most relevant parts of this representation for instrument identification were determined for each instrument. In addition, instruments that were confused with each other led to non-overlapping spectrotemporal modulation regions, suggesting that musical instrument timbres are characterized by specific spectrotemporal modulations.

A cross cultural analysis of musical timbre perception:comparison between Chinese and Western culture

Objective:To assess the musical timbre perception ability of normal hearing subjects with different cultural background, and to analyze the correlation between cultural differences and timbre perception.Method: Forty Chinese normal hearing adults(CNH) and 40 normal hearing adults from other countries(FNH) participated in this study.The Mu.S.I.C test was applied for the assessment.The correct rate of musical instrument recognition and numbers of instrument identification of each participant were recorded. Instrument Identification Test was performed with tenor，soprano ,and 5 representative musical instruments from different families of musical instruments ,and the Instrument Detection Test was performed with mixing sounds of musical instruments.Result:The correct rate of musical instrument recognition in FNH and CNH was（95.58±8.90）% and（89.40±13.13）%（P<0.05）, respectively.The correct rate of numbers of instrument detection in FNH and CNH was （80.50±16.32）% and（73.25±21.17）%（P>0.05）,respectively. The correct rate of CNH was lower than those of FNH for the trumpet, the oboe and the xylophone. Participants in both 2 groups were more easily failed to recognize the oboe and the trumpet compared the other musical instruments, especially in CNH group.Conclusion:Subjects with Western culture performed better than Chinese subjects on timbre perception. Cultural differences have a significant impact on musical timbre perception.

Neural correlates of recognition and naming of musical instruments.

Retrieval of lexical (names) and conceptual (semantic) information is frequently impaired in individuals with neurological damage. One category of items that is often affected is musical instruments. However, distinct neuroanatomical correlates underlying lexical and conceptual knowledge for musical instruments have not been identified. We used a neuropsychological approach to explore the neural correlates of knowledge retrieval for musical instruments. A large sample of individuals with focal brain damage (N = 298), viewed pictures of 16 musical instruments and were asked to name and identify each instrument. Neuroanatomical data were analyzed with a proportional MAP-3 method to create voxelwise lesion proportion difference maps. Impaired naming (lexical retrieval) of musical instruments was associated with damage to the left temporal pole and inferior pre- and postcentral gyri. Impaired recognition (conceptual knowledge retrieval) of musical instruments was associated with a more broadly and bilaterally distributed network of regions, including ventromedial prefrontal cortices, occipital cortices, and superior temporal gyrus. The findings extend our understanding of how musical instruments are processed at neural system level, and elucidate factors that may explain why brain damage may or may not produce anomia or agnosia for musical instruments. Our findings also help inform broader understanding of category-related knowledge mapping in the brain, as musical instruments possess several characteristics that are similar to various other categories of items: They are inanimate and highly manipulable (similar to tools), produce characteristic sounds (similar to animals), and require fine-grained visual differentiation between each other (similar to people). (PsycINFO Database Record

Indian Musical Instruments Recognition Using LPC and LPI

Most Significant Music Industry is today widely supported by Research in the area of Musical information Processing. In the past decade the growth of Digital Music over internet and our day-to-day life has forced researcher to explorethe diverse area of Musical Instrument Identification, Retrieval, Classification and Recognition of large sets of music. In our paper we have explored the ignored area of Indian Musical Instrument Recognition (IMIR) and attempted to build the database for the same. An innovative technique to classify the IMIR using the Modified Linear Predictor Coefficient (LPC) features and Log-Power-Intensity (LPI) is discussed. The proposed method has been tested with experimental results and technique has an accuracy of 96.11% for instrument and 92.45 for individual sound.

Biomimetic spectro-temporal features for music instrument recognition in isolated notes and solo phrases.

The identity of musical instruments is reflected in the acoustic attributes of musical notes played with them. Recently, it has been argued that these characteristics of musical identity (or timbre) can be best captured through an analysis that encompasses both time and frequency domains; with a focus on the modulations or changes in the signal in the spectrotemporal space. This representation mimics the spectrotemporal receptive field (STRF) analysis believed to underlie processing in the central mammalian auditory system, particularly at the level of primary auditory cortex. How well does this STRF representation capture timbral identity of musical instruments in continuous solo recordings remains unclear. The current work investigates the applicability of the STRF feature space for instrument recognition in solo musical phrases and explores best approaches to leveraging knowledge from isolated musical notes for instrument recognition in solo recordings. The study presents an approach for parsing solo performances into their individual note constituents and adapting back-end classifiers using support vector machines to achieve a generalization of instrument recognition to off-the-shelf, commercially available solo music.

Indian Musical Instrument Recognition using Modified LPC Features

Indian Classical Music is considered very diverse and distinct area of music across the globe. It has its indistinct melodies especially made up of unique musical instruments. It uses a wide variety of Musical Instruments to achieve this feat. In last two decades, researchers are actively associated with human perception towards the study of Musical Instruments. In this paper, we have proposed an innovative method to classify the Indian Musical Instrument Recognition (IMIR) technique using the Modified Linear Predictor Coefficient (LPC) features. The Classification algorithm has adopted Linear Discriminant Analysis (LDA). The proposed method has been tested with nine kinds of musical instruments. The research project involved the identification of musical sounds with experimental results using the present technique which has an accuracy of 93.04%. General Terms Indian Musical Instrument Recognition (IMIR), Linear Predictor Coefficient (LPC), Linear Discriminant Analysis (LDA), Best First Decision Tree, Temporal Features, Spectral Features, Monophonic, Polyphonic audio signal.

Human Perception and Recognition of Musical Instruments: A Review

Musical Instrument is the soul of music. Musical Instrument and Player are the two fundamental component of Music. In the past decade the growth of a new research field targeting the Musical Instrument Identification, Retrieval, Classification, Recognition and management of large sets of music is known as Music Information Retrieval. An attempt to review the methods, features and database is done.

Recognition of Music and Melody in Patients With Cochlear Implants, Using a New Programming Approach for Frequency Assignment

Introduction and objectiveMusic is a universal, cross-cultural phenomenon. Perception and enjoyment of music are still not solved with current technological objectives of cochlear implants. The objective of this article was to advance the development and validation of a method of programming of cochlear implants that implements a frequency allocation strategy. We compared standard programming vs frequency programming in every subject. MethodsWe studied a total of 40 patients with cochlear implants. Each patient was programmed with an optimal version of the standard program, using the Custom Sound Suite 3.2 cochlear platform. Speech tests in silence were performed using syllable word lists from the protocol for the assessment of hearing in the Spanish language. Patients implanted bilaterally were tested in both ears at the same time. For assessing music listening habits we used the Munich Music Questionnaire and “MACarena” (minimum auditory capability) software. ResultsAll patients achieved better results in recognition, instrument tests and tonal scales with frequency programming (P<.005). Likewise, there were better results with frequency programming in recognising harmonics and pitch test (P<.005). ConclusionsFrequency programming achieves better perception and recognition results in patients in comparison with standard programming. Bilateral stimulation patients have better perception of musical patterns and better performance in recognition of tonal scales, harmonics and musical instruments compared with patients with unilateral stimulation. Modification and frequency allocation during programming allows decreased levels of current intensity and increase the dynamic range, which allows mapping of each audio band less obtrusively and improves the quality of representation of the signal.

Reconocimiento auditivo musical y melódico en pacientes con implante coclear, mediante nuevo método de programación de asignación frecuencial

Introduction and objectiveMusic is a universal, cross-cultural phenomenon. Perception and enjoyment of music are still not solved with current technological objectives of cochlear implants. The objective of this article was to advance the development and validation of a method of programming of cochlear implants that implements a frequency allocation strategy. We compared standard programming vs frequency programming in every subject. MethodsWe studied a total of 40 patients with cochlear implants. Each patient was programmed with a optimal version of the standard program, using the Custom Sound Suite 3.2 cochlear platform. Speech tests in quiet were performed using syllable word lists from the protocol for the assessment of hearing in the Spanish language. Patients implanted bilaterally were tested in both ears at the same time. For assessing music listening habits we used the Munich Music Questionnaire and «MACarena»(minimum auditory capability) software. ResultsAll patients achieved better results in recognition, instrument tests and tonal scales with frequency programming (P<.005). Likewise, there were better results with frequency programming in recognising harmonics and pitch test (P<.005). ConclusionsFrequency programming achieves better perception and recognition results in patients in comparison with standard programming. Bilateral stimulation patients have better perception of musical patterns and better performance in recognition of tonal scales, harmonics and musical instruments compared with patients with unilateral stimulation. Modification and frequency allocation during programming allows decreased levels of current intensity and increase the dynamic range, which allows mapping of each audio band less obtrusively and improves the quality of representation of the signal.

Hybridization of Fractional Fourier Transform and Acoustic Features for Musical Instrument Recognition

This paper presents musical instrument recognition for isolated music sound signals using hybridization of fractional fourier transform (FRFT) based features with timbrel (acoustic) features using feed forward neural network. The FRFT based features which is named as fractional MFCC are computed by replacing conventional discrete fourier transform in mel frequency cepstral coefficient (MFCC) with discrete FRFT. Hybrid features are obtained by effectively combining Fractional MFCC with timbrel features such as temporal, spectral and cepstral features. Feed forward neural network with back propagation algorithm has been used to test the performance of system and results were compared in terms of recognition accuracy and number of features. Proposed feature out performs over individual and other traditional features proposed in the literature. The experimentation is performed on isolated musical sounds of 19 musical instruments covering four different instrument families. The system is tested on benchmarked McGill University musical sound database.