Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets

Acoustic events often have a visual counterpart. Knowledge of visual information can aid the understanding of complex auditory scenes, even when only a stereo mixdown is available in the audio domain, \eg identifying which musicians are playing in large musical ensembles. In this paper, we consider a vision-based approach to note onset detection. As a case study we focus on challenging, real-world clarinetist videos and carry out preliminary experiments on a 3D convolutional neural network based on multiple streams and purposely avoiding temporal pooling. We release an audiovisual dataset with 4.5 hours of clarinetist videos together with cleaned annotations which include about 36,000 onsets and the coordinates for a number of salient points and regions of interest. By performing several training trials on our dataset, we learned that the problem is challenging. We found that the CNN model is highly sensitive to the optimization algorithm and hyper-parameters, and that treating the problem as binary classification may prevent the joint optimization of precision and recall. To encourage further research, we publicly share our dataset, annotations and all models and detail which issues we came across during our preliminary experiments.Comment: Proceedings of the First International Conference on Deep Learning and Music, Anchorage, US, May, 2017 (arXiv:1706.08675v1 [cs.NE]

Explaining Black-Box Models through Counterfactuals

We present CounterfactualExplanations.jl: a package for generating Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box models in Julia. CE explain how inputs into a model need to change to yield specific model predictions. Explanations that involve realistic and actionable changes can be used to provide AR: a set of proposed actions for individuals to change an undesirable outcome for the better. In this article, we discuss the usefulness of CE for Explainable Artificial Intelligence and demonstrate the functionality of our package. The package is straightforward to use and designed with a focus on customization and extensibility. We envision it to one day be the go-to place for explaining arbitrary predictive models in Julia through a diverse suite of counterfactual generators.Comment: 13 pages, 9 figures, originally published in The Proceedings of the JuliaCon Conferences (JCON

The Biased Journey of MSD_AUDIO.ZIP

The equitable distribution of academic data is crucial for ensuring equal research opportunities, and ultimately further progress. Yet, due to the complexity of using the API for audio data that corresponds to the Million Song Dataset along with its misreporting (before 2016) and the discontinuation of this API (after 2016), access to this data has become restricted to those within certain affiliations that are connected peer-to-peer. In this paper, we delve into this issue, drawing insights from the experiences of 22 individuals who either attempted to access the data or played a role in its creation. With this, we hope to initiate more critical dialogue and more thoughtful consideration with regard to access privilege in the MIR community

One Deep Music Representation to Rule Them All? : A comparative analysis of different representation learning strategies

Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach. In this approach, it is possible to reuse the output of a pre-trained neural network as the basis for a new learning task. The underlying hypothesis is that if the initial and new learning tasks show commonalities and are applied to the same type of input data (e.g. music audio), the generated deep representation of the data is also informative for the new task. Since, however, most of the networks used to generate deep representations are trained using a single initial learning source, their representation is unlikely to be informative for all possible future tasks. In this paper, we present the results of our investigation of what are the most important factors to generate deep representations for the data and learning tasks in the music domain. We conducted this investigation via an extensive empirical study that involves multiple learning sources, as well as multiple deep learning architectures with varying levels of information sharing between sources, in order to learn music representations. We then validate these representations considering multiple target datasets for evaluation. The results of our experiments yield several insights on how to approach the design of methods for learning widely deployable deep data representations in the music domain.Comment: This work has been accepted to "Neural Computing and Applications: Special Issue on Deep Learning for Music and Audio

Aplikasi Sistem Informasi Administrasi Pada PD. Meteor Motor

PD. Meteor Motor is a company specialized in the sale of Honda motorcycles. The stock of the company has been run in a computerized, but other business processes (purchases and sales) run manually. The purpose of this thesis is to develop a web-based information system that is accessible by company management in operational activities. The application interface is designed using CSS and HTML. This information system is built by using PHP, Java, and HTML programming language. As for data storage uses MySQL. The results based on questionnaires on the application of information systems at PT. Meteor Motor, several conclusions can be drawn that 80% of respondents said that the design of the application system is quite easy to use, and as many as 20% of respondents said that the design of the application is easy to use, in addition as much as 60% of respondents said application system sufficient to meet the company\u27s business processes, and as many as 40% of respondents said application system meets the company\u27s business processes, as well as 60% of respondents said application system is easily accessible, and 40% of respondents said application system is very easily accessibl

Endogenous Macrodynamics in Algorithmic Recourse

Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the goal is to find valid counterfactuals for an individual instance that fulfill various desiderata. The ability of such counterfactuals to handle dynamics like data and model drift remains a largely unexplored research challenge. There has also been surprisingly little work on the related question of how the actual implementation of recourse by one individual may affect other individuals. Through this work, we aim to close that gap. We first show that many of the existing methodologies can be collectively described by a generalized framework. We then argue that the existing framework does not account for a hidden external cost of recourse, that only reveals itself when studying the endogenous dynamics of recourse at the group level. Through simulation experiments involving various state-of the-art counterfactual generators and several benchmark datasets, we generate large numbers of counterfactuals and study the resulting domain and model shifts. We find that the induced shifts are substantial enough to likely impede the applicability of Algorithmic Recourse in some situations. Fortunately, we find various strategies to mitigate these concerns. Our simulation framework for studying recourse dynamics is fast and opensourced.Comment: 12 pages, 11 figures. Originally published at the 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). IEEE holds the copyrigh