Deep convolutional neural network for environmental sound classification via dilation

Roy, Sanjiban Sekhar; Mihalache, Sanda Florentina; Pricop, Emil; Rodrigues, Nishant

doi:10.3233/jifs-219283

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…Moreover, we may use other neural network structures as known in the literature [54,55], e.g., using sample-level filters instead of frame-level input representations [56], and trying other approaches to music feature extraction, e.g., including derivation of rhythm, melody, and harmony and determining their weights by employing the exponential analytic hierarchy process (AHP) [57]. Lastly, the model proposed may be tested with audio signals other than music, such as classification of urban sounds [58].…”

Section: Discussionmentioning

confidence: 99%

Musical Instrument Identification Using Deep Learning Approach

Blaszke

Kostek

2022

Sensors

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The work aims to propose a novel approach for automatically identifying all instruments present in an audio excerpt using sets of individual convolutional neural networks (CNNs) per tested instrument. The paper starts with a review of tasks related to musical instrument identification. It focuses on tasks performed, input type, algorithms employed, and metrics used. The paper starts with the background presentation, i.e., metadata description and a review of related works. This is followed by showing the dataset prepared for the experiment and its division into subsets: training, validation, and evaluation. Then, the analyzed architecture of the neural network model is presented. Based on the described model, training is performed, and several quality metrics are determined for the training and validation sets. The results of the evaluation of the trained network on a separate set are shown. Detailed values for precision, recall, and the number of true and false positive and negative detections are presented. The model efficiency is high, with the metric values ranging from 0.86 for the guitar to 0.99 for drums. Finally, a discussion and a summary of the results obtained follows.

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Section: Discussionmentioning

confidence: 99%

Musical Instrument Identification Using Deep Learning Approach

Blaszke

Kostek

2022

Sensors

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“…A deep convolutional neural network is a typical deep learning algorithm formed by connecting neural networks that contain multiple layers. The more layers, the more parameters the model contains, and the more complex the model is [15][16][17][18]. The well-trained deep convolutional neural network can automatically learn and extract the key information that can reflect the characteristics of the data based on multi-dimensional complex data and then establish a complex mapping relationship that can reflect the complex mapping relationship between the patterns of the essential characteristics of the data.…”

Section: Intelligent Recommendation Module For Learning Materialsmentioning

confidence: 99%

Current Situation and Mode Innovation of Physical Education Teaching in Colleges and Universities under the Perspective of Deep Learning

Wang

2023

Applied Mathematics and Nonlinear Sciences

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This paper analyzes the current situation of physical education teaching in colleges and universities from the perspective of deep learning and innovates the teaching methodology. A multidimensional data array is used to quantify students’ interests and hobbies to create a recommendation module for intelligent sports learning materials. Using the SIFT feature extraction algorithm, identify the extreme value point of the detection target and develop the sports target extraction module. Through distance transformation and morphological features, determine the skeleton image features to construct a college sports teaching model using deep learning. The results show that the model in this paper enables the coverage rate of sports courses to reach over 97.20%, with a fluctuation of no more than 1.5%. Deep learning has contributed to some innovation in college sports teaching.

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“…In order to deal with these issues in CNN, another most popular learning technique has been adopted by researchers called Transfer Learning (TL), which is widely used for different applications in the medical field [10,11]. This technique makes use of various parameters and resources of a pretrained model.…”

Section: Introductionmentioning

confidence: 99%

Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging

Pallawi

Singh

2023

Cognitive Comp and Systems

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Alzheimer's disease is a type of progressive neurological disorder which is irreversible and the patient suffers from severe memory loss. This disease is the seventh largest cause of death across the globe. As yet there is no cure for this disease, the only way to control it is its early diagnosis. Deep Learning techniques are mostly preferred in classification tasks because of their high accuracy over a large dataset. The main focus of this paper is on fine‐tuning and evaluating the Deep Convolutional Networks for Alzheimer's disease classification. An empirical analysis of various deep learning‐based neural network models has been done. The architectures evaluation includes InceptionV3, ResNet with 50 layers and 101 layers and DenseNet with 169 layers. The dataset has been taken from Kaggle which is publicly available and comprises of four classes which represents the various stages of Alzheimer's disease. In our experiment, the accuracy of DenseNet consistently improved with the increase in the number of epochs resulting in a 99.94% testing accuracy score better than the rest of the architectures. Although the results obtained are satisfactory, but for future research, we can apply transfer learning on other deep models like Inception V4, AlexNet etc., to increase accuracy and decrease computational time. Also, in future we can work on other datasets like ADNI or OASIS and use Positron emitted tomography, diffusion tensor imaging neuroimages and their combinations for better result.

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Deep convolutional neural network for environmental sound classification via dilation

Cited by 9 publications

References 27 publications

Musical Instrument Identification Using Deep Learning Approach

Musical Instrument Identification Using Deep Learning Approach

Current Situation and Mode Innovation of Physical Education Teaching in Colleges and Universities under the Perspective of Deep Learning

Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging

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