A fuzzy hierarchy-based pattern matching technique for melody classification

Kaur, Chandanpreet; Kumar, Ravi

doi:10.1007/s00500-018-3383-7

Cited by 3 publications

(3 citation statements)

References 35 publications

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“…The classification of error rate (CEE) is referred to as the objective or fitness function that evaluates the performances to select the features and provides solutions. The fitness function is evaluated using (16). From each of the datasets, the instances of the dataset are spitted into training and testing of 70% and 30% samples.…”

Section: Spatial Value Spatial Bounding Process and Spatial Updatementioning

confidence: 99%

“…Step 2: The population fitness is used in (16) and the positions are retained with the fittest population having the source position 𝑓 𝑗 which are known as the global initial population.…”

Section: Hyper Parameter Optimization Using Weighted Salp Swarm Optim...mentioning

confidence: 99%

“…The proposed technique has built a model that has a possible combination with overall hyper parameters that are provided also the model evaluated and selected the architecture for producing better results [15]. Kaur and Kumar [16] developed a fuzzy-based hierarchy-based pattern matching model for classification based on melody. The improved pattern matching technique was used for the classification by using fuzzy analytical hierarchy process.…”

Section: Introductionmentioning

confidence: 99%

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Raga classification using enhanced spatial bound whale optimization algorithm

Gowrishankar

Bhajantri

2023

IJEECS

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A raga is a unique set of notes with certain rules that carefully followed, retain and protect its purity and produce amazing musical effects. An automated raga transcription and identification is important for computational musicology, which is an important step for musicology for indexing, classifying, and recommending tunes. In the present research, the audio features such as mel frequency cepstrum coefficients (MFCCs), spectral flux, short time energy, audio feature extractor, and spectral centroid features are used for the prediction of a raga. The model showed more complexity which means it required lots of training data. The proposed enhanced spatial bound whale optimization algorithm (ESBWOA) is used that overcome the feature selection problem of high dimensional features. In addition to this, a weighted salp swarm algorithm (SSA) is used for selecting the tone-based features from the ragas based on amplitude or each raga sample. The features were fed for bidirectional long short-term memory (Bi-LSTM) network, which enhanced the success rate for raga identification and classification. The present research uses CompMusic dataset in the research work where 9 classes for Carnatic music and 7 classes in Hindustani music are considered for the classification of ragas.

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Section: Spatial Value Spatial Bounding Process and Spatial Updatementioning

confidence: 99%

Section: Hyper Parameter Optimization Using Weighted Salp Swarm Optim...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Raga classification using enhanced spatial bound whale optimization algorithm

Gowrishankar

Bhajantri

2023

IJEECS

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Granulation of ecological networks under fuzzy soft environment

Akram

Luqman

2020

Soft Comput

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Degree based models of granular computing under fuzzy indiscernibility relations

Akram

Al-Kenani

Luqman

2021

MBE

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<abstract><p>The aim of this research work is to put forward fuzzy models of granular computing based on fuzzy relation and fuzzy indiscernibility relation. Thanks to fuzzy information granulation to provide multi-level visualization of problems that include uncertain information. In such a granulation, fuzzy sets and fuzzy graphs help us to represent relationships among granules, groups or clusters. We consider the fuzzy indiscernibility relation of a fuzzy knowledge representation system ($ \mathcal{I} $). We describe the granular structures of $ \mathcal{I} $, including discernibility, core, reduct and essentiality of $ \mathcal{I} $. Then we examine the contribution of these structures to granular computing. Moreover, we introduce certain granular structures using fuzzy graph models and discuss degree based model of fuzzy granular structures. Granulation of network models based on fuzzy information effectively handles real life data which possesses uncertainty and vagueness. Finally, certain algorithms of proposed models are developed and implemented to solve real life problems involving uncertain granularities. We also present a concise comparison of the models developed in our work with other existing methodologies.</p></abstract>

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A fuzzy hierarchy-based pattern matching technique for melody classification

Cited by 3 publications

References 35 publications

Raga classification using enhanced spatial bound whale optimization algorithm

Raga classification using enhanced spatial bound whale optimization algorithm

Granulation of ecological networks under fuzzy soft environment

Degree based models of granular computing under fuzzy indiscernibility relations

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