The document discusses research on identifying successful algorithms for measuring melodic similarity that can be used in music information retrieval applications. It describes a listening experiment conducted with 34 subjects to rate the similarity of melodic variations on a theme. Correlation analysis was used to check the consistency of subject ratings. Two main algorithms are discussed that measure melodic similarity based on pitch differences between notes over time windows, with weights applied based on duration or metrical stress. The researchers are analyzing the results to determine which musical features lead to the most accurate algorithms according to human similarity judgments.
Understanding dimensions is of utmost importance as it helps us in studying the nature of physical quantities mathematically. The basic concept of dimensions is that we can add or subtract only those quantities which have same dimensions. Copy the link given below and paste it in new browser window to get more information on Dimensional Analysis and its Applications www.askiitians.com/iit-jee-physics/general-physics/dimensional-analysis-and-its-applications/
Advanced cosine measures for collaborative filteringLoc Nguyen
Cosine similarity is an important measure to compare two vectors for many researches in data mining and information retrieval. In this research, cosine measure and its advanced variants for collaborating filtering (CF) are evaluated. Cosine measure is effective but it has a drawback that there may be two end points of two vectors which are far from each other according to Euclidean distance, but their cosine is high. This is negative effect of Euclidean distance which decreases accuracy of cosine similarity. Therefore, a so-called triangle area (TA) measure is proposed as an improved version of cosine measure. TA measure uses ratio of basic triangle area to whole triangle area as reinforced factor for Euclidean distance so that it can alleviate negative effect of Euclidean distance whereas it keeps simplicity and effectiveness of both cosine measure and Euclidean distance in making similarity of two vectors. TA is considered as an advanced cosine measure. TA and other advanced cosine measures are tested with other similarity measures. From experimental results, TA is not a preeminent measure but it is better than traditional cosine measures in most cases and it is also adequate to real-time application. Moreover, its formula is simple too.
The expressions or formulae which tell us how and which of the fundamental quantities are present in a physical quantity are known as the Dimensional Formula of the Physical Quantity. The equations obtained when we equal a physical quantity with its dimensional formulae are called Dimensional Equations. Copy the link given below and paste it in new browser window to get more information on Dimensional Formulae and Dimensional Equations www.askiitians.com/iit-jee-physics/general-physics/dimensional-formulae-and-dimensional-equations/
Schema matching using machine learningShruti Jadon
Databases are the core part of any product, with new companies getting merged and creating cross-functional teams, database merging has become one of the crucial tasks to maintain the efficiency of the product. In the process of database merging, generally, Schemas get redesigned, and Schema Matching is the first step for it. Schema Matching is a method of finding attributes in database fields that are either similar to each other linguistically or represent the same information. Indeed various approaches exist to do automated schema matching, but they fail in different scenarios, such as different language-based fields. In this paper, We explored existing methods (centroid, self-organizing maps, etc.) and proposed a hybrid approach framework that uses both provided data and the schema name to solve a one-to-one schema matching problem. We also introduced the creation of a global dictionary method for one-to-many matching. In the global dictionary method, we map similar fields to the same groups using feature extraction and clustering. We have performed experiments using both techniques and compared them to existing approaches. We were able to attain an F1 Score of 0.71 using our hybrid approach, an increase from 0.69 using the existing centroid approach.
Armon; 2009 yılından beri etkinlik sektöründe ihtiyaçlara profesyonel çözümler üreten, markaları son kullanıcıyla, firmaları müşterileri ile etkin olarak buluşturan, teknolojiyi sanat ile, tasarımı brief ile harmanlayan, eğlenceyi ve çalışmayı odak noktası haline getirmiş bir etkinlik operasyonu firmasıdır.
Armon is an event operations company in Turkey.
Understanding dimensions is of utmost importance as it helps us in studying the nature of physical quantities mathematically. The basic concept of dimensions is that we can add or subtract only those quantities which have same dimensions. Copy the link given below and paste it in new browser window to get more information on Dimensional Analysis and its Applications www.askiitians.com/iit-jee-physics/general-physics/dimensional-analysis-and-its-applications/
Advanced cosine measures for collaborative filteringLoc Nguyen
Cosine similarity is an important measure to compare two vectors for many researches in data mining and information retrieval. In this research, cosine measure and its advanced variants for collaborating filtering (CF) are evaluated. Cosine measure is effective but it has a drawback that there may be two end points of two vectors which are far from each other according to Euclidean distance, but their cosine is high. This is negative effect of Euclidean distance which decreases accuracy of cosine similarity. Therefore, a so-called triangle area (TA) measure is proposed as an improved version of cosine measure. TA measure uses ratio of basic triangle area to whole triangle area as reinforced factor for Euclidean distance so that it can alleviate negative effect of Euclidean distance whereas it keeps simplicity and effectiveness of both cosine measure and Euclidean distance in making similarity of two vectors. TA is considered as an advanced cosine measure. TA and other advanced cosine measures are tested with other similarity measures. From experimental results, TA is not a preeminent measure but it is better than traditional cosine measures in most cases and it is also adequate to real-time application. Moreover, its formula is simple too.
The expressions or formulae which tell us how and which of the fundamental quantities are present in a physical quantity are known as the Dimensional Formula of the Physical Quantity. The equations obtained when we equal a physical quantity with its dimensional formulae are called Dimensional Equations. Copy the link given below and paste it in new browser window to get more information on Dimensional Formulae and Dimensional Equations www.askiitians.com/iit-jee-physics/general-physics/dimensional-formulae-and-dimensional-equations/
Schema matching using machine learningShruti Jadon
Databases are the core part of any product, with new companies getting merged and creating cross-functional teams, database merging has become one of the crucial tasks to maintain the efficiency of the product. In the process of database merging, generally, Schemas get redesigned, and Schema Matching is the first step for it. Schema Matching is a method of finding attributes in database fields that are either similar to each other linguistically or represent the same information. Indeed various approaches exist to do automated schema matching, but they fail in different scenarios, such as different language-based fields. In this paper, We explored existing methods (centroid, self-organizing maps, etc.) and proposed a hybrid approach framework that uses both provided data and the schema name to solve a one-to-one schema matching problem. We also introduced the creation of a global dictionary method for one-to-many matching. In the global dictionary method, we map similar fields to the same groups using feature extraction and clustering. We have performed experiments using both techniques and compared them to existing approaches. We were able to attain an F1 Score of 0.71 using our hybrid approach, an increase from 0.69 using the existing centroid approach.
Armon; 2009 yılından beri etkinlik sektöründe ihtiyaçlara profesyonel çözümler üreten, markaları son kullanıcıyla, firmaları müşterileri ile etkin olarak buluşturan, teknolojiyi sanat ile, tasarımı brief ile harmanlayan, eğlenceyi ve çalışmayı odak noktası haline getirmiş bir etkinlik operasyonu firmasıdır.
Armon is an event operations company in Turkey.
1. Study of the sharing economy in the accommodation industry, ie. Online Residential Rental ("ORR")
2. Competitive analysis between the ORR and the Traditional Hotel Business
3. A new business model of ORR run by a hotel management company, namely Accor Hotels (“Accor”)
The kusc classical music dataset for audio key findingijma
In this paper, we present a benchmark dataset based on the KUSC classical music collection and provide
baseline key-finding comparison results. Audio key finding is a basic music information retrieval task; it
forms an essential component of systems for music segmentation, similarity assessment, and mood
detection. Due to copyright restrictions and a labor-intensive annotation process, audio key finding
algorithms have only been evaluated using small proprietary datasets to date. To create a common base for
systematic comparisons, we have constructed a dataset comprising of more than 3,000 excerpts of classical
music. The excerpts are made publicly accessible via commonly used acoustic features such as pitch-based
spectrograms and chromagrams. We introduce a hybrid annotation scheme that combines the use of title
keys with expert validation and correction of only the challenging cases. The expert musicians also provide
ratings of key recognition difficulty. Other meta-data include instrumentation. As demonstration of use of
the dataset, and to provide initial benchmark comparisons for evaluating new algorithms, we conduct a
series of experiments reporting key determination accuracy of four state-of-the-art algorithms. We further
show the importance of considering factors such as estimated tuning frequency, key strength or confidence
value, and key recognition difficulty in key finding. In the future, we plan to expand the dataset to include
meta-data for other music information retrieval tasks.
MediaEval 2015 - UNIZA System for the "Emotion in Music" task at MediaEval 20...multimediaeval
In this working notes paper, we present the UNIZA system for the recognition of dynamic music emotional dimensions arousal and valence. The developed system is based on Support Vector Regression with Radial Basis kernel function. We selected 2 sets of features using stochastic evolutionary optimization algorithms namely Genetic Algorithm and Particle Swarm Algorithm. The models score the average Root Mean Square Error 0.3 605 for the valence dimension and 0.2540 for the arousal dimension.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
1. Study of the sharing economy in the accommodation industry, ie. Online Residential Rental ("ORR")
2. Competitive analysis between the ORR and the Traditional Hotel Business
3. A new business model of ORR run by a hotel management company, namely Accor Hotels (“Accor”)
The kusc classical music dataset for audio key findingijma
In this paper, we present a benchmark dataset based on the KUSC classical music collection and provide
baseline key-finding comparison results. Audio key finding is a basic music information retrieval task; it
forms an essential component of systems for music segmentation, similarity assessment, and mood
detection. Due to copyright restrictions and a labor-intensive annotation process, audio key finding
algorithms have only been evaluated using small proprietary datasets to date. To create a common base for
systematic comparisons, we have constructed a dataset comprising of more than 3,000 excerpts of classical
music. The excerpts are made publicly accessible via commonly used acoustic features such as pitch-based
spectrograms and chromagrams. We introduce a hybrid annotation scheme that combines the use of title
keys with expert validation and correction of only the challenging cases. The expert musicians also provide
ratings of key recognition difficulty. Other meta-data include instrumentation. As demonstration of use of
the dataset, and to provide initial benchmark comparisons for evaluating new algorithms, we conduct a
series of experiments reporting key determination accuracy of four state-of-the-art algorithms. We further
show the importance of considering factors such as estimated tuning frequency, key strength or confidence
value, and key recognition difficulty in key finding. In the future, we plan to expand the dataset to include
meta-data for other music information retrieval tasks.
MediaEval 2015 - UNIZA System for the "Emotion in Music" task at MediaEval 20...multimediaeval
In this working notes paper, we present the UNIZA system for the recognition of dynamic music emotional dimensions arousal and valence. The developed system is based on Support Vector Regression with Radial Basis kernel function. We selected 2 sets of features using stochastic evolutionary optimization algorithms namely Genetic Algorithm and Particle Swarm Algorithm. The models score the average Root Mean Square Error 0.3 605 for the valence dimension and 0.2540 for the arousal dimension.
http://ceur-ws.org/Vol-1436/
http://www.multimediaeval.org
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
The goal of this project is to build a classifier able to predict whether a song is happy or sad analysing its lyrics. Most of the research on music classi cation is based on features
obtained by audio signals. However, the exploration of lyrics alone as a source of information can be relevant in music
classi cation. It is an interesting problem and it has not been widely explored in the literature.
Behavior study of entropy in a digital image through an iterative algorithmijscmcj
Image segmentation is a critical step in computer vision tasks constituting an essential issue for pattern recognition and visual interpretation. In this paper, we study the behavior of entropy in digital images through an iterative algorithm of mean shift filtering. The order of a digital image in gray levels is defined. The behavior of Shannon entropy is analyzed and then compared, taking into account the number of iterations of our algorithm, with the maximum entropy that could be achieved under the same order. The use of equivalence classes it induced, which allow us to interpret entropy as a hyper-surface in real m dimensional space. The difference of the maximum entropy of order n and the entropy of the image is used to group the the iterations, in order to caractrizes the performance of the algorithm.
A new parallel bat algorithm for musical note recognition IJECEIAES
Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (developed bat algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer.
Music Recommendation System with User-based and Item-based Collaborative Filt...ijeei-iaes
Internet and E-commerce are the generators of abundant of data, causing information Overloading. The problem of information overloading is addressed by Recommendation Systems (RS). RS can provide suggestions about a new product, movie or music etc. This paper is about Music Recommendation System, which will recommend songs to users based on their past history i.e. taste. In this paper we proposed a collaborative filtering technique based on users and items. First user-item rating matrix is used to form user clusters and item clusters. Next these clusters are used to find the most similar user cluster or most similar item cluster to a target user. Finally songs are recommended from the most similar user and item clusters. The proposed algorithm is implemented on the benchmark dataset Last.fm. Results show that the performance of proposed method is better than the most popular baseline method.
Similarity Measures for Traditional Turkish Art MusicCSCJournals
Pitch histograms are frequently used for a wide range of applications in music information retrieval (MIR) which mainly focus on western music. However there are significant differences between pitch spaces of traditional Turkish art music (TTAM) and western music which prevent to apply current methods. In this sense comparison of pitch histograms for TTAM corresponds to the research domain in pattern recognition: finding an appropriate similarity measure in relation with the metric axioms and characteristics of the data. Therefore we have evaluated various similarity measures frequently used in histogram comparison such as L1-norm, L2-norm, histogram intersection, correlation coefficient measures and earth mover’s distance (EMD) for TTAM. Consequently we have discussed one of the problems of the domain, about measures regarding overlap or/and non-overlap between ordinal type histograms and presented an improved version of EMD for TTAM.
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Identifying Successful Melodic Similarity Algorithms for use in Music
1. Identifying Successful Melodic Similarity Algorithms for use in Music Retrieval Margaret Cahill Donncha Ó Maidín University of Limerick, Ireland. [email_address]
13. The Algorithms where: k = the time windows of the score p 1 , p 2 = pitch values of the first and second melodies in a window w k = the weight associated with that time window totaldur = the duration processed Figure 2: Algorithm - Ó Maidín 1998
![Identifying Successful Melodic Similarity Algorithms for use in Music Retrieval Margaret Cahill Donncha Ó Maidín University of Limerick, Ireland. [email_address]](https://image.slidesharecdn.com/identifying-successful-melodic-similarity-algorithms-for-use-in-music-17572/85/Identifying-Successful-Melodic-Similarity-Algorithms-for-use-in-Music-1-320.jpg)
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