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Found 31 papers, 16 papers with code
Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements for trustworthy AI.
Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving
This paper addresses the often overlooked issue of fairness in the autonomous driving domain, particularly in vision-based perception and prediction systems, which play a pivotal role in the overall functioning of Autonomous Vehicles (AVs).
Behind Recommender Systems: the Geography of the ACM RecSys Community
Recommender Systems filter this information into manageable streams or feeds, adapted to our personal needs or preferences.
Use case cards: a use case reporting framework inspired by the European AI Act
"Use case cards" allows framing and contextualising use cases in an effective way, and we hope this methodology can be a useful tool for policy makers and providers for documenting use cases, assessing the risk level, adapting the different requirements and building a catalogue of existing usages of AI.
Fairness and Diversity in Information Access Systems
Among the seven key requirements to achieve trustworthy AI proposed by the High-Level Expert Group on Artificial Intelligence (AI-HLEG) established by the European Commission (EC), the fifth requirement ("Diversity, non-discrimination and fairness") declares: "In order to achieve Trustworthy AI, we must enable inclusion and diversity throughout the entire AI system's life cycle.
Assessing the Impact of Music Recommendation Diversity on Listeners: A Longitudinal Study
We present the results of a 12-week longitudinal user study wherein the participants, 110 subjects from Southern Europe, received on a daily basis Electronic Music (EM) diversified recommendations.
Liability regimes in the age of AI: a use-case driven analysis of the burden of proof
New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better.
Diversity in the Music Listening Experience: Insights from Focus Group Interviews
In this study, we interview several listeners about the role that diversity plays in their listening experience, trying to get a better understanding of how they interact with music recommendations.
EIHW-MTG DiCOVA 2021 Challenge System Report
This paper aims to automatically detect COVID-19 patients by analysing the acoustic information embedded in coughs.
Assessing Algorithmic Biases for Musical Version Identification
Version identification (VI) systems now offer accurate and scalable solutions for detecting different renditions of a musical composition, allowing the use of these systems in industrial applications and throughout the wider music ecosystem.
LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters
Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production.
Perceptions of Diversity in Electronic Music: the Impact of Listener, Artist, and Track Characteristics
Shared practices to assess the diversity of retrieval system results are still debated in the Information Retrieval community, partly because of the challenges of determining what diversity means in specific scenarios, and of understanding how diversity is perceived by end-users.
Investigating the efficacy of music version retrieval systems for setlist identification
The setlist identification (SLI) task addresses a music recognition use case where the goal is to retrieve the metadata and timestamps for all the tracks played in live music events.
Less is more: Faster and better music version identification with embedding distillation
Version identification systems aim to detect different renditions of the same underlying musical composition (loosely called cover songs).
A Deep Learning Based Analysis-Synthesis Framework For Unison Singing
Unison singing is the name given to an ensemble of singers simultaneously singing the same melody and lyrics.
Multiple F0 Estimation in Vocal Ensembles using Convolutional Neural Networks
This paper addresses the extraction of multiple F0 values from polyphonic and a cappella vocal performances using convolutional neural networks (CNNs).
Exploring Artist Gender Bias in Music Recommendation
Music Recommender Systems (mRS) are designed to give personalised and meaningful recommendations of items (i. e. songs, playlists or artists) to a user base, thereby reflecting and further complementing individual users' specific music preferences.
Conditioned Source Separation for Music Instrument Performances
In music source separation, the number of sources may vary for each piece and some of the sources may belong to the same family of instruments, thus sharing timbral characteristics and making the sources more correlated.
Sound Audio and Speech Processing
Vocoder-Based Speech Synthesis from Silent Videos
Both acoustic and visual information influence human perception of speech.
Multi-channel U-Net for Music Source Separation
However, Conditioned U-Net (C-U-Net) uses a control mechanism to train a single model for multi-source separation and attempts to achieve a performance comparable to that of the dedicated models.
Addressing multiple metrics of group fairness in data-driven decision making
The Fairness, Accountability, and Transparency in Machine Learning (FAT-ML) literature proposes a varied set of group fairness metrics to measure discrimination against socio-demographic groups that are characterized by a protected feature, such as gender or race. Such a system can be deemed as either fair or unfair depending on the choice of the metric.
Measuring Diversity of Artificial Intelligence Conferences
The lack of diversity of the Artificial Intelligence (AI) field is nowadays a concern, and several initiatives such as funding schemes and mentoring programs have been designed to overcome it.
Neural Percussive Synthesis Parameterised by High-Level Timbral Features
We present a deep neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds.
Accurate and Scalable Version Identification Using Musically-Motivated Embeddings
The version identification (VI) task deals with the automatic detection of recordings that correspond to the same underlying musical piece.
Ranked #4 on
Cover song identification
on YouTube350
The emotions that we perceive in music: the influence of language and lyrics comprehension on agreement
Our goal is to understand the influence of lyrics comprehension on the perception of emotions and use this information to improve Music Emotion Recognition (MER) models.
A Case Study of Deep-Learned Activations via Hand-Crafted Audio Features
We also propose a technique for measuring the similarity between activation maps and audio features which typically presented in the form of a matrix, such as chromagrams or spectrograms.
A Framework for Multi-f0 Modeling in SATB Choir Recordings
We observe, however, that the scenario of multiple singers for each choir part (i. e. unison singing) is far more challenging.
Deep Learning for Singing Processing: Achievements, Challenges and Impact on Singers and Listeners
This paper summarizes some recent advances on a set of tasks related to the processing of singing using state-of-the-art deep learning techniques.
A multidisciplinary task-based perspective for evaluating the impact of AI autonomy and generality on the future of work
This paper presents a multidisciplinary task approach for assessing the impact of artificial intelligence on the future of work.
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
The workshop gathered an interdisciplinary group of experts to establish the state of the art research in the field and a list of future research challenges to be addressed on the topic of human and machine intelligence, algorithm's potential impact on human cognitive capabilities and decision making, and evaluation and regulation needs.
Timbre Analysis of Music Audio Signals with Convolutional Neural Networks
The focus of this work is to study how to efficiently tailor Convolutional Neural Networks (CNNs) towards learning timbre representations from log-mel magnitude spectrograms.
Sound
