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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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A mass spectrum is the graphical representation of the relative abundance of the charged fragments in an analyte plotted against their mass-to-charge ratio (m/z). The plot's x-axis represents the ratio of the mass of the charged fragment to the number of charges it carries. The y axis of the plot represents the relative abundance of each charged species. The relative abundance is calculated from the signal intensity of each charged species recorded at the detector. The most intense signal (the...
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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The non-destructive nature and ability to provide valuable chemical information make IR spectroscopy a versatile technique with broad applications in various scientific and industrial fields. IR spectroscopy is commonly used to identify and characterize organic and inorganic compounds. It provides information about the functional groups present in a molecule and the bonding between atoms. This helps in the structural elucidation of compounds during organic synthesis, pharmaceutical research,...
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SpectroFusionNet是一种CNN的方法,利用光谱融合用于电吉他演奏识别.

Ganesh Kumar Chellamani1, Aishwarya N2, Chandhana C1

  • 1Department of ECE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai, India.

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概括

深度学习模型SpectroFusionNet准确地识别了使用Mel-Frequency Cepstral Coefficients (MFCC) 和Gammatone光谱的电吉他技巧. 这种自动化系统实现了音乐信息检索的高精度.

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吉他演奏识别 吉他演奏识别轻量级的深度学习是轻量级的.ML分类器 ML分类器实时音频处理 实时音频处理频谱图的聚变光谱是指聚变光谱的光谱.

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科学领域:

  • 音乐信息检索 音乐信息检索
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 信号处理 信号处理

背景情况:

  • 音乐是人类表达中不可或缺的一种通用语言.
  • 音乐乐器和技术的自动识别是一个不断增长的领域.
  • 电吉他演奏涉及复杂的技巧,很难分类.

研究的目的:

  • 介绍SpectroFusionNet,这是一个用于自动识别电吉他演奏技术的深度学习框架.
  • 探索各种频谱提取和特征融合策略,以改善分类.
  • 评估关于不同吉他声音类别的拟议框架的性能.

主要方法:

  • 提取Mel-Frequency Cepstral Coefficients (MFCC),连续波形变换 (CWT) 和Gammatone光谱图. 这些光谱图中的Mel-Frequency Cepstral Coefficients (MFCC) 和Gammatone光谱图中的CWT.
  • 使用轻量级深度学习模型 (MobileNetV2,InceptionV3,ResNet50) 来单独处理光谱图.
  • 早期和晚期融合策略的应用,然后使用九种机器学习模型 (SVM,MLP,随机森林等) 进行分类. ) 的情况.

主要成果:

  • MFCC-Gammatone晚期融合策略实现了最高的性能:99.12%的准确性,100%的精度和100%的回忆 across9个类.
  • 在单个光谱图处理中,ResNet50显示出更好的性能.
  • 在实时音频数据集上,SpectroFusionNet以70.9%的准确度证明了其在现实世界中的适用性.

结论:

  • 在SpectroFusionNet的帮助下,可以有效地自动识别电吉他演奏技巧.
  • 晚期的MFCC和Gammatone光谱的融合为分类提供了优越的特征表示.
  • 该框架显示了在音乐技术和分析领域的现实应用的潜力.