Musical key extraction using diffusion maps

The problem of learning from seismic recordings has been studied for years. There is a growing interest of developing automatic mechanisms for identifying the properties of a seismic event. One main motivation is the ability have a reliable identification of manmade explosions. The availability of multiple high dimensional observations has increased the use of machine learning techniques in a variety of fields. In this work, we propose to use a kernel-fusion based dimensionality reduction framework for generating meaningful seismic representations from raw data. The proposed method is tested on 2023 events that were recorded in Israel and in Jordan. The method achieves promising results in classification of event type as well as in estimating the location of the event. The proposed fusion and dimensionality reduction tools may be applied to other types of geophysical data.

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“…where C ∈ [2,3]. Alternative methods such as [36], [37] have demonstrated similar results in our experiments.…”

Section: B Setting the Kernel's Bandwidthsupporting

confidence: 73%

“…This results in a Markovian process that travels in the high-dimensional space only in areas where the sampled data exists. The method has been demonstrated useful when applied to audio signals [37], image editing [40], medical data analysis [41] and other types of data sets.…”

Section: Non-linear Dimensionality Reductionmentioning

confidence: 99%

Multiview Kernels for Low-Dimensional Modeling of Seismic Events

Lindenbaum

Bregman²,

Rabin

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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“…Since we aim for a system capable of generating compelling accompaniments given a wide scope of music, we selected three datasets that cover distinct styles of music including monophonic/polyphonic and monotimbral/multitimbral examples. Furthermore, a comparison with existing keyfinding methods can be established for the Beatles dataset [Gómez 2006;Lindenbaum et al 2015].…”

Section: Objective Analysismentioning

confidence: 99%

“…Several studies have used the Beatles dataset to test their key finding methods. From these studies a direct comparison can be established with Gómez [2006], , and Lindenbaum et al [2015], which also use the complete Beatles' dataset. They report 70.4%, 63%, and 66.5% of correct key estimates, respectively.…”

Section: Objective Analysismentioning

confidence: 99%

Harmony Generation Driven by a Perceptually Motivated Tonal Interval Space

et al. 2016

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We present D'accord, a generative music system for creating harmonically compatible accompaniments of symbolic and musical audio inputs with any number of voices, instrumentation, and complexity. The main novelty of our approach centers on offering multiple ranked solutions between a database of pitch configurations and a given musical input based on tonal pitch relatedness and consonance indicators computed in a perceptually motivated Tonal Interval Space. Furthermore, we detail a method to estimate the key of symbolic and musical audio inputs based on attributes of the space, which underpins the generation of key-related pitch configurations. The system is controlled via an adaptive interface implemented for Ableton Live, MAX, and Pure Data, which facilitates music creation for users regardless of music expertise and simultaneously serves as a performance, entertainment, and learning tool. We perform a threefold evaluation of D'accord, which assesses the level of accuracy of our key-finding algorithm, the user enjoyment of generated harmonic accompaniments, and the usability and learnability of the system.

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“…Many methods such as Principal Component Analysis (PCA) [1], Multidimensional Scaling (MDS) [2], Local Linear Embedding [3], Laplacian Eigenmaps [4], Diffusion Maps (DM) [5] and more have been proposed to achieve dimensionality reduction that preserve its data coherency. Exploiting the low dimensional representation yields various applications such as face recognition that is based on Laplacian Eigenmaps [6], Non-linear independent component analysis with DM [7], Musical Key extraction using DM [8], and many more. The DM framework extends and enhances ideas from other methods by utilizing a stochastic Markov matrix that is based on local affinities between multidimensional data points to identify a lower dimension representation for the data.…”

Section: Introductionmentioning

confidence: 99%

Multi-view diffusion maps

et al. 2020

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In this paper, a reduced dimensionality representation is learned from multiple views of the processed data. These multiple views can be obtained, for example, when the same underlying process is observed using several different modalities, or measured with different instrumentation. The goal is to effectively utilize the availability of such multiple views for various purposes such as non-linear embedding, manifold learning, spectral clustering, anomaly detection and non-linear system identification. The proposed method, which is called multi-view, exploits the intrinsic relation within each view as well as the mutual relations between views. This is achieved by defining a cross-view model in which an implied random walk process is restrained to hop between objects in the different views. This multi-view method is robust to scaling and it is insensitive to small structural changes in the data. Within this framework, new diffusion distances are defined to analyze the spectra of the implied kernels. The applicability of the multi-view approach is demonstrated for clustering, classification and manifold learning using both artificial and real data. 2 The problem of learning from two views has been studied in the field of spectral clustering. Most of these studies have been focused on classification and clustering that are based on spectral characteristics of the data while using two or more sampled views. Some approaches, which address this problem, are Bilinear Model [9], Partial Least Squares [10] and Canonical Correlation Analysis [11]. These methods are powerful for learning the relation between different views but do not provide separate insights or combined into the low dimensional geometry or structure of each view. Recently, a few kernel based methods (e.g [12]) propose a model of co-regularizing kernels in both views in a way that resembles joint diagonalization. It is done by searching for an orthogonal transformation that maximizes the diagonal terms of the kernel matrices obtained from all views. A penalty term, which incorporates the disagreement between clusters from the views, was added. Their algorithm is based on alternating maximization procedure. A mixture of Markov chains is proposed in [13] to model multiple views in order to apply spectral clustering. It deals with two cases in graph theory: directed and undirected graph where the second case is related to our work. This approach converges the undirected graph problem to a Markov chains averaging where each is constructed separately within the views. A way to incorporate a given multiple metrics for the same data using a cross diffusion process is described in [14]. They define a new diffusion distance which is useful for classification, clustering or retrieval tasks. However, the proposed process is not symmetrical thus does not allow to compute an embedding. An iterative algorithm for spectral clustering is proposed in [15]. The idea is to iteratively modify each view using the representation of the other view. The problem of two manifolds, ...

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Musical key extraction using diffusion maps

Cited by 9 publications

References 19 publications

Multiview Kernels for Low-Dimensional Modeling of Seismic Events

Multiview Kernels for Low-Dimensional Modeling of Seismic Events

Harmony Generation Driven by a Perceptually Motivated Tonal Interval Space

Multi-view diffusion maps

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