1,915 research outputs found
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
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