Speech and music classification using spectrogram based statistical descriptors and extreme learning machine

Birajdar, Gajanan K.; Patil, Mukesh D.

doi:10.1007/s11042-018-6899-z

Cited by 17 publications

(6 citation statements)

References 65 publications

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“…In formula (8), w is consistent with the definition in TF-IDF, which means the frequency of a certain characteristic word in the current sample. e variable h represents the position factors of the verse, chorus, sublimation, and ending.…”

Section: Extraction Of Location Factors Of Feature Words In Lyricsmentioning

confidence: 56%

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[Retracted] Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

2021

Complexity

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In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value.

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Section: Extraction Of Location Factors Of Feature Words In Lyricsmentioning

confidence: 56%

“…Due to the massive investment of scholars and the attention of all occupations, there are many current music detection algorithms [7][8][9][10][11][12]. Compared with other types of algorithms, music detection algorithms based on artificial neural networks and support vector machines are the most commonly used.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

2021

Complexity

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“…In the process of listening and discrimination, college students can present a multidimensional effect through their ears on pitch, rhythm, beat, strength, interval, range, and timbre. At the same time, teachers should help college students build their sound perception ability in the process of listening and distinguishing, and pay attention to the details of music sound [20][21][22]. The cultivation of these music perception abilities is essential in chorus teaching [23].…”

Section: Multivoice Music Perceptionmentioning

confidence: 99%

Effect of Multivoice Chorus on Interpersonal Communication Disorder

Lei

2022

Occupational Therapy International

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Objective. As a type of music therapy, multipart chorus does not have high requirements for participants, and the basic level can be high or low. For college students, it is more likely to participate. Exploring the effect of multivoice chorus intervention on college students’ mental health can provide reference for school psychological correction work. This study uses multivoice chorus as a means, combined with certain group psychological counseling techniques, to try to explore a new model for improving the mental health of college students. Method. Fifty college students in prison were selected as subjects and randomly divided into the control group and experimental group. The multivoice chorus method was used to conduct an intervention test on the mental health of the college students in the experimental group, and the observation was carried out for 3 months. Before and after the intervention, the anxiety self-rating scale, the trust scale, the social avoidance and distress scale, the shyness scale, the UCLA loneliness scale, and the college student psychological test personality subtest were used for psychological testing and evaluation. Results/Discussion. College students can actively recognize and reflect on their own behavior, and objectively evaluate others. Through the cognition and management of emotions, college students can handle personal emotions well in interpersonal communication. In the later stage of multivoice chorus, they can help each other, establish a mutual assistance system, help each other, and discuss and learn interpersonal communication skills together, so that college students can learn good interpersonal communication experience and skills from others in various interactions, achieve self-help, and promote college students to work together. In the process of interfering with interpersonal communication disorder, simple psychological counseling has also improved the phenomenon of interpersonal communication disorder, but the experimental effect is far from the effect of the combination of music education and psychological counseling, and it is relatively unstable in the stability test. In the specific operation, in the purely psychological counseling process, college students with interpersonal barriers do not actively cooperate with the intervention, the operating atmosphere is relatively dull, and the teachers are time-consuming and labor-intensive during the psychological counseling process, but the results are minimal. The only effect is also worn out within a few weeks, and the stability is poor. It can be seen that music education has played a pivotal role as a medium in the intervention of interpersonal communication disorders, which can make the intervention process easier, more effective, and more stable.

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“…In this paper, a Probabilistic Latent Graph-Based Ranking Approach is employed for feature selection. This algorithm ranks the features as per relevance by observing all subsets of the features and studying the convergence properties of power series of matrices [44]. ILFS technique works by assigning score of importance to each feature considering all features as nodes on the graph modelling a pairwise relationship between all possible combinations of features by weighing the edges joining them.…”

Section: Feature Importance Using Roc Analysismentioning

confidence: 99%

Hilbert spectrum based features for speech/music classification

2022

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Automatic Speech/Music classification uses different signal processing techniques to categorize multimedia content into different classes. The proposed work explores Hilbert Spectrum (HS) obtained from different AM-FM components of an audio signal, also called Intrinsic Mode Functions (IMFs) to classify an incoming audio signal into speech/music signal. The HS is a twodimensional representation of instantaneous energies (IE) and instantaneous frequencies (IF) obtained using Hilbert Transform of the IMFs. This HS is further processed using Mel-filter bank and Discrete Cosine Transform (DCT) to generate novel IF and Instantaneous Amplitude (IA) based cepstral features. Validations of the results were done using three databases-Slaney Database, GTZAN and MUSAN database. To evaluate the general applicability of the proposed features, extensive experiments were conducted on different combination of audio files from S&S, GTZAN and MUSAN database and promising results are achieved. Finally, performance of the system is compared with performance of existing cepstral features and previous works in this domain.

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Speech and music classification using spectrogram based statistical descriptors and extreme learning machine

Cited by 17 publications

References 65 publications

[Retracted] Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

[Retracted] Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

Effect of Multivoice Chorus on Interpersonal Communication Disorder

Hilbert spectrum based features for speech/music classification

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