Tune classification using multilevel recursive local alignment algorithms

This paper investigates several enhancements to two well-established local alignment algorithms in the context of their use for melodic similarity. It uses the annotated dataset from the well-known Meertens Tune Collection to provide a ground truth and the research aim to answer the question, to what extent do these enhancements improve the quality of the algorithms? In the results, recursive application of the alignment algorithms, applied to a multilevel representation of the melodies, is shown to be very effective for improving the accuracy of the classification of the tunes into families. However, the ideas should be equally applicable to music search and melodic matching

Constructing proximity graphs to explore similarities in large-scale melodic datasets

This paper investigates the construction of proximity graphs in order to allow users to explore similarities in melodic datasets. A key part of this investigation is the use of a multilevel framework for measuring similarity in symbolic musical representations. The basis of the framework is straightforward: initially each tune is normalised and then recursively coarsened, typically by removing weaker off-beats, until the tune is reduced to a skeleton representation with just one note per bar. Melodic matching can then take place at every level: the multilevel matching implemented here uses recursive variants of local alignment algorithms, but in principle a variety of similarity measures could be used. The multilevel framework is also exploited with the use of early termination heuristics at coarser levels, both to reduce computational complexity and, potentially, to enhance the matching qualitatively. The results of the matching algorithm are then used to construct proximity graphs which are displayed as part of an online interface for users to explore melodic similarities within a corpus of tunes

A statistical analysis of the ABC music notation corpus: exploring duplication

This paper presents a statistical analysis of the abc music notation corpus. The corpus contains around 435,000 transcriptions of which just over 400,000 are folk and traditional music. There is significant duplication within the corpus and so a large part of the paper discusses methods to assess the level of duplication and the analysis then indicates a headline figure of over 165,000 distinct folk and traditional melodies. The paper also describes TuneGraph, an online, interactive user interface for exploring tune variants, based on visualising the proximity graph of the underlying melodies

TuneGraph, an online visual tool for exploring melodic similarity

This paper presents TuneGraph, an online visual tool for exploring melodic similarity. The underlying data comes from a large index of online music, all transcribed in abc notation, and TuneGraph uses a melodic similarity metric to derive a proximity graph representing similarities within the index. A rich but dense graph is built and then sparsfied weak, non-essential edges. From this a local graph is extracted for each vertex, aimed at indicating close variants of, and similar melodies to, the underlying tune represented by the vertex. Finally an interactive user interface displays each local graph on that tune's webpage, allowing the user to explore melodically similar tunes

The compound graph: a case study for community visualisation in social networks

This paper builds on previous work which aimed at providing a graph-based visual exploration of melodic relationships (tune families) within collections of traditional music. Here, using a community detection algorithm, potential tune families can be readily identified. However, the richer the information contained in the graph, the more difficult it is for the visualisation algorithms to operate successfully. Therefore, an approach is proposed which uses modified versions of the graph both to enhance the community detection results and, more importantly, restructure the graph, by creating a compound graph, to reveal the communities visually. Finally, the wider applicability of the technique is considered

Multilevel melodic matching

This paper describes a multilevel algorithm for matching tunes when performing inexact searches in symbolic mu-sical data. The basis of the algorithm is straightforward: initially each tune in the search database is normalised and quantised and then recursively coarsened, typically by removing weaker off-beats, until the tune is reduced to a skeleton representation with just one note per bar. The same process is applied to the search query and melodic matching between query and data can then take place at every level. The algorithm implemented here uses the longest common substring algorithm at each level, but in principle a variety of similarity measures could be used. The multilevel framework allows inexact matches to occur by identifying similarities at course levels and is also exploited with the use of early termination heuristics at coarser levels, both to reduce computational complexity and to enhance the matching qualitatively. Experimenta-tion demonstrates the effectiveness of the approach for inexact melodic searches within a corpus of tunes

Failure mode & effect analysis for improving data veracity and validity

Failure Mode & Effect Analysis (FMEA) is a method that has been used to improve reliability of products, processes, designs, and software for different applications, including electronics manufacturing. In this paper we propose a modification of this method to extend its application for data veracity and validity improvement. The proposed DVV-FMEA method is based on engineering features and in addition, provides transparency and understandability of the data and its pre-processing, making it reproducible and trustful

Failure mode & effect analysis and another methodology for improving data veracity and validity

Failure Mode & Effect Analysis (FMEA) is a method that has been used to improve reliability of products, processes, designs, and software for different applications. In this paper we extend its usage for data veracity and validity improvement in the context of big data analysis and discuss its application in an electronics manufacturing test procedure which consists of a sequence of tests. Finally, we describe another methodology, developed as a result of the DVV-FMEA application which is aimed at improving the tests' repeatability and failure detection capabilities as well as monitoring their reliability

Asignación de cabezales radio a procesadores banda base mediante redes neuronales de grafos.

In 5G networks, Cloud-Radio Access Network (C-RAN) architecture divides legacy base stations into Radio Remote Heads (RRH) and Base Band Units (BBU). RRHs transmit and receive radio signals, whereas BBUs process those signals. Thus, BBUs can be centralized in cloud processing centers serving different groups of RRHs. An adequate allocation of RRHs to BBUs is essential to guarantee C-RAN performance. With the latest advances in machine learning, this task can be automatically addressed through supervised learning. This paper proposes a methodology for allocating RRHs to BBUs in heterogeneous cellular networks relying on graph partitioning through a graph neural network. Model performance is assessed over a dataset built with a radio planning tool that implements a realistic Long-Term Evolution (LTE) heterogeneous network. Results have shown that the proposed method improves performance of a patented state-of-theart tool based on graph partitioning.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech