Abstract
In order to improve the efficiency and accuracy of collecting theater music data and recognizing genres, and solve the problem that a single feature in traditional algorithms cannot efficiently classify music genres, the technology of collecting data based on the Internet of Things (IoT) and Deep Belief Network (DBN) is firstly proposed for application of collecting music data and recognizing genres. The study firstly introduces the scheme of collecting music data based on the Internet of Things (IoT) and the theoretical basis of the recognition and classification of music genres by DBN. Second, the study focuses on the construction and improvement of the music genre recognition algorithm under DBN. In other words, the algorithm is optimized by adding Dropout and momentum to the network, and the optimal network model after training is implemented. And finally, the effectiveness of the algorithm is confirmed by experiment and research. The experimental results show that the efficiency of the optimized algorithm of identifying the music genres in the music library reaches 75.8%, which is far better than the traditional classic algorithms in the past. It is concluded that the technology of music data collection and genre recognition based on IoT and DBN has strong advantages, which will greatly contribute to reducing the workload of manual collection and identification of classification features, and improve efficiency. Meanwhile, the optimized algorithm model can also be extended to other fields.
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References
Zalkow F, Müller M (2020) Learning low-dimensional embeddings of audio shingles for cross-version retrieval of classical music[J]. Appl Sci 10(1):19
Tian F, Yang C et al (2020) Deep belief network-hidden Markov model based nonlinear equalizer for VCSEL based optical interconnect[J]. Sci China (Inf Sci) 63(6):155–163
Myna AN, Deepthi K, Shankar SV (2020) Hybrid recommender system for music information retrieval[J]. J Comput Theor Nanosci 17(9–10):4145–4149
Nilashi M, Ahmadi H, Sheikhtaheri A et al (2020) Remote Tracking of Parkinson’s Disease Progression Using Ensembles of Deep Belief Network and Self-Organizing Map[J]. Expert Syst Appl 159:113562
Rahman JS, Gedeon T, Caldwell S et al (2021) Towards effective music therapy for mental health care using machine learning tools: Human affective reasoning and music genres[J]. J Artif Intell Soft Comput Res 11(1):5–20
Sujanaa J, Palanivel S (2020) HOG-based emotion recognition using one-dimensional convolutional neural network[J]. ICTACT J Image Video Process 11(2):2310–2315
Rajesh S, Nalini NJ (2021) Recognition of musical instrument using deep learning techniques[J]. Int J Inf Retriev Res (IJIRR) 11(4):41–60
Tang S, Chen W, Jin L et al (2020) SWCNTs-based MEMS gas sensor array and its pattern recognition based on deep belief networks of gases detection in oil-immersed transformers[J]. Sens Actuators 312:127998
Haridas AV, Marimuthu R, Sivakumar VG, et al (2020) Correction to: Emotion recognition of speech signal using Taylor series and deep belief network based classification[J]. Evol Intell, 1–1
Wenwen WC, Zhang F et al (2020) Extracting soil moisture from Fengyun-3D medium resolution spectral imager-II imagery by using a deep belief network[J]. J Meteorol Res 34(04):92–103
Srivastava N, Dubey S (2020) Moth monarch optimization-based deep belief network in deception detection system[J]. Sadhana, 45(1)
Shahabi H, Shirzadi A, Ronoud S et al (2021) Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm[J]. Geosci Front 12(3):101100
Meng-Shi LI, Da YU, Chen ZM et al (2019) Fault diagnosis and isolation method for wind turbines based on deep belief network[J]. Dianji yu Kongzhi Xuebao/Electric Machines Control 23(2):114–122
Asadi S, Ronoud S (2019) An evolutionary deep belief network extreme learning-based for breast cancer diagnosis[J]. Soft Comput 23(24):13139–13159
Heydari MJ, Ghidary SS (2019) Cross-modal motion regeneration using multimodal deep belief network[J]. J Visual Commun Image Represent 58:245–260
Nickfarjam AM, Ebrahimpour-Komleh H (2019) Multi-input 1-dimensional deep belief network[J]. Multimedia Tools Appl 78(13):17739–17761
Schindler A. Multi-Modal Music Information Retrieval: Augmenting Audio-Analysis with Visual Computing for Improved Music Video Analysis[J]. arXiv preprint https://arxiv.org/abs/2002.00251, 2020.
Li Y, Zheng W (2021) Emotion recognition and regulation based on stacked sparse auto-encoder network and personalized reconfigurable music[J]. Mathematics 9(6):593
Abd Al-Hattab Y, Zaki H F, Shafie A A. Rethinking environmental sound classification using convolutional neural networks: optimized parameter tuning of single feature extraction[J]. Neural Comput Appl, 2021: 1–12
Raheel A, Majid M, Alnowami M et al (2020) Physiological sensors based emotion recognition while experiencing tactile enhanced multimedia[J]. Sensors 20(14):4037
Sarkar R, Choudhury S, Dutta S et al (2020) Recognition of emotion in music based on deep convolutional neural network[J]. Multimedia Tools Appl 79(1):765–783
Rajib S, Sombuddha C, Saikat D et al (2020) Recognition of emotion in music based on deep convolutional neural network[J]. Multimedia Tools Appl 79(1–2):765–783
Ntalampiras S (2020) Toward language-agnostic speech emotion recognition[J]. J Audio Eng Soc 68(1/2):7–13
Sheeba PT, Murugan S (2019) Fuzzy dragon deep belief neural network for activity recognition using hierarchical skeleton features[J]. Evol Intel 24:1–18
Yazdani A F, Mehr A B, Showkatyan I, et al (2021) Fluid temperature detection based on its sound with a deep learning approach [J]. Int J Image Graphic Signal Process (IJIGSP), 13(1)
Mcneely-Whtie K, Cleary AM (2019) Music recognition without identification and its relation to déjà entendu: a study using “Piano Puzzlers”[J]. New Ideas Psychol 55:50–57
Ballantine C (2020) Against populism: music, classification, Genre[J]. Twentieth-Century Music 17(2):247–267
Raja MW, Anand S, Allan D (2019) Advertising music: an alternative atmospheric stimulus to retail music[J]. Int J Retail Distribut Manag 47(8):872–892
Baro A, Riba P, Calvo-Zaragoza J et al (2019) From optical music recognition to handwritten music recognition: a baseline[J]. Pattern Recogn Lett 123(MAY):1–8
Jakubec M, Chmulik M (2019) Automatic music genre recognition for in-car infotainment[J]. Trans Res Procedia 40:1364–1371
Wagener GL, Berning M, Costa AP et al (2020) Effects of emotional music on facial emotion recognition in children with autism spectrum disorder (ASD)[J]. J Autism Dev Disord 2:1–10
Ranjana P, Varsha S (1818) Eliyas S (2021) Iot based smart garbage collection using RFID andsensors. J Phys Conf Series 1818(1):012225
Kang KD, Kang H, Ilankoon I et al (2019) Electronic waste collection systems using Internet of Things (IoT): household electronic waste management in Malaysia[J]. J Cleaner Product 252:119801
Albaaj A, Jattiot M, Manciaux L et al (2019) Hyperketolactia occurrence before or after artificial insemination is associated with a decreased pregnancy per artificial insemination in dairy cows[J]. J Dairy Sci 102(9):8527–8536
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Xiang, H. The collection of theater music data and genre recognition under the internet of things and deep belief network. J Supercomput 78, 9307–9325 (2022). https://doi.org/10.1007/s11227-021-04261-x
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DOI: https://doi.org/10.1007/s11227-021-04261-x