Toward intelligent music information retrieval

Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of intelligent music information retrieval. Huron points out that since the preeminent functions of music are social and psychological, the most useful characterization would be based on four types of information: genre, emotion, style,and similarity. This paper introduces Daubechies Wavelet Coefficient Histograms (DWCH)for music feature extraction for music information retrieval. The histograms are computed from the coefficients of the db/sub 8/ Daubechies wavelet filter applied to 3 s of music. A comparative study of sound features and classification algorithms on a dataset compiled by Tzanetakis shows that combining DWCH with timbral features (MFCC and FFT), with the use of multiclass extensions of support vector machine,achieves approximately 80% of accuracy, which is a significant improvement over the previously known result on this dataset. On another dataset the combination achieves 75% of accuracy. The paper also studies the issue of detecting emotion in music. Rating of two subjects in the three bipolar adjective pairs are used. The accuracy of around 70% was achieved in predicting emotional labeling in these adjective pairs. The paper also studies the problem of identifying groups of artists based on their lyrics and sound using a semi-supervised classification algorithm. Identification of artist groups based on the Similar Artist lists at All Music Guide is attempted. The semi-supervised learning algorithm resulted in nontrivial increases in the accuracy to more than 70%. Finally, the paper conducts a proof-of-concept experiment on similarity search using the feature set.     

边缘计算: 现状与展望

近年来,信息技术的不断变革伴随数据量的急剧爆发,使主流的云计算解决方案面临实时性差、带宽受限、高能耗、维护费用高、隐私安全等问题. 边缘智能的出现与快速发展有效缓解了此类问题,它将用户需求处理下沉到边缘,避免了海量数据在网络中的流动,得到越来越多的关注. 由于边缘计算中资源性能普遍较低,通过资源实现协同推理正成为热点.通过对边缘智能发展的趋势分析,得出边缘协同推理目前仍处于增长期,还未进入稳定发展期. 因此,在对边缘协同推理进行充分调研的基础上,将边缘协同推理划分为智能化方法与协同推理架构2个部分,分别对其中涉及到的关键技术进行纵向归纳整理,并从动态场景角度出发,对每种关键技术进行深入分析,对不同关键技术进行横向比较以及适用场景分析.最后对动态场景下的边缘协同推理给出值得研究的若干发展方向.

     

Note segmentation and quantization for music information retrieval

Much research in music information retrieval has focused on query-by-humming systems, which search melodic databases using sung queries. The database retrieval aspect of such systems has received considerable attention, but query processing and the melodic representation have not been examined as carefully. Common methods for query processing are based on musical intuition and historical momentum rather than specific performance criteria; existing systems often employ rudimentary note segmentation or coarse quantization of note estimates. In this work, we examine several alternative query processing methods as well as quantized melodic representations. One common difficulty with designing query-by-humming systems is the coupling between system components. We address this issue by measuring the performance of the query processing system both in isolation and coupled with a retrieval system. We first measure the segmentation performance of several note estimators. We then compute the retrieval accuracy of an experimental query-by-humming system that uses the various note estimators along with varying degrees of pitch and duration quantization. The results show that more advanced query processing can improve both segmentation performance and retrieval performance, although the best segmentation performance does not necessarily yield the best retrieval performance. Further, coarsely quantizing the melodic representation generally degrades retrieval accuracy.     

Capturing the workflows of music information retrieval for repeatability and reuse

Many solutions for the reuse and re-purposing of Music Information Retrieval (MIR) methods, and the tools implementing those methods, have been introduced over recent years. Proposals for achieving interoperability between systems have ranged from shared software libraries and interfaces, through common frameworks and portals, to standardised file formats and metadata. Here we assess these solutions for their suitability to be reused and combined as repurposable components within assemblies (or workflows) that can be used in novel and possibly more ambitious ways. Reuse and repeatability also have great implications for the process of MIR research: the encapsulation of any algorithm and its operation—including inputs, parameters, and outputs—is fundamental to the repeatability and reproducibility of an experiment. This is desirable both for the open and reliable evaluation of algorithms and for the advancement of MIR by building more effectively upon prior research. At present there is no clear best practice widely adopted by the field. Based upon our analysis of contemporary systems and their adoption we reflect as to whether this should be considered a failure. Are there limits to interoperability unique to MIR, and how might they be overcome? Beyond workflows how much research context can, and should, be captured? We frame our assessment within the emerging notion of Research Objects for reproducible research in other domains, and describe how their adoption could serve as a route to reuse in MIR.     

The neglected user in music information retrieval research

Personalization and context-awareness are highly important topics in research on Intelligent Information Systems. In the fields of Music Information Retrieval (MIR) and Music Recommendation in particular, user-centric algorithms should ideally provide music that perfectly fits each individual listener in each imaginable situation and for each of her information or entertainment needs. Even though preliminary steps towards such systems have recently been presented at the “International Society for Music Information Retrieval Conference” (ISMIR) and at similar venues, this vision is still far away from becoming a reality. In this article, we investigate and discuss literature on the topic of user-centric music retrieval and reflect on why the breakthrough in this field has not been achieved yet. Given the different expertises of the authors, we shed light on why this topic is a particularly challenging one, taking computer science and psychology points of view. Whereas the computer science aspect centers on the problems of user modeling, machine learning, and evaluation, the psychological discussion is mainly concerned with proper experimental design and interpretation of the results of an experiment. We further present our ideas on aspects crucial to consider when elaborating user-aware music retrieval systems.     

State of the art and challenges in mineral processing control

The objective of process control in the mineral industry is to optimise the recovery of the valuable minerals, while maintaining the quality of the concentrates delivered to the metal extraction plants. The paper presents a survey of the control approaches for ore size reduction and mineral separation processes. The present limitations of the measurement instrumentation are discussed, as well as the methods to upgrade the information delivered by the sensors. In practice, the overall economic optimisation goal must be hierarchically decomposed into simpler control problems. Model-based and AI methods are reviewed, mainly for grinding and flotation processes, and classified as mature, active or emerging.     

Privacy protection in pervasive systems: State of the art and technical challenges

Pervasive and mobile computing applications are dramatically increasing the amount of personal data released to service providers as well as to third parties. Data includes geographical and indoor positions of individuals, their movement patterns as well as sensor-acquired data that may reveal individuals’ physical conditions, habits, and, in general, information that may lead to undesired consequences like unsolicited advertisement or more serious ones like discrimination and stalking.In this survey paper, at first we consider representative classes of pervasive applications, and identify the requirements they impose in terms of privacy and trade-off with service quality. Then, we review the most prominent privacy preservation approaches, we discuss and summarize them in terms of the requirements.Finally, we take a more holistic view of the privacy problem by discussing other aspects that turn out to be crucial for the widespread adoption of privacy enhancing technologies. We discuss technical challenges like the need for tools augmenting the awareness of individuals and to capture their privacy preferences, as well as legal and economic challenges. Indeed, on one side privacy solutions must comply to ethical and legal requirements, and not prevent profitable business models, while on the other side it is unlikely that privacy preserving solutions will become practical and effective without new regulations.     

Live digital,remember digital: State of the art and research challenges

The so called trend “live digital, remember digital” is acquiring higher relevance within the international research community, due to its several appealing challenges in a multitude of different fields within the Information and Communication Technologies. Today, many people live daily connected to the Internet through their mobile phones, laptops, tablets, etc. and the need to audit or log every single digital interaction emerges in many environments. By seamlessly recording those digital interactions and storing them in a privacy-preserving fashion, a number of benefits are brought to end users, like the provision of user-tailored services, amongst many others. In this paper we will particularly focus on the study of the security and privacy challenges within this field, as well as on the analysis of the currently existing solutions addressing these issues and we will propose an architecture for the so called live digital systems.     

Control of connected and automated vehicles: State of the art and future challenges

Autonomous driving technology pledges safety, convenience, and energy efficiency. Its challenges include the unknown intentions of other road users: communication between vehicles and with the road infrastructure is a possible approach to enhance awareness and enable cooperation. Connected and automated vehicles (CAVs) have the potential to disrupt mobility, extending what is possible with driving automation and connectivity alone. Applications include real-time control and planning with increased awareness, routing with micro-scale traffic information, coordinated platooning using traffic signals information, and eco-mobility on demand with guaranteed parking. This paper introduces a control and planning architecture for CAVs, and surveys the state of the art on each functional block therein; the main focus is on techniques to improve energy efficiency. We provide an overview of existing algorithms and their mutual interactions, we present promising optimization-based approaches to CAVs control and identify future challenges.     

Designing hypermedia information retrieval systems for multimedia art catalogues

Abstract

Multimedia art catalogues present a number of recurring characteristics in both the type of data and the users involved, and in the type of operations required. Typical data include textual catalogue cards manageable through an information retrieval system (IRS), as well as textual captions, images, video, and speech, organizable as a hypermedia network. In this paper we propose a model for the design of these applications, and discuss its effectiveness in improving the quality of the application. The model exploits a multi-level design approach to organize data and access structures of the IRS, while the Entity-Relationship (E-R) approach, tailored to model hypermedia applications, is adopted to describe the structure of the documents and their links.     

An effective music information retrieval method using three-dimensional continuous DP

This paper describes a music information retrieval system that uses humming as the key for retrieval. Humming is an easy way for a user to input a melody. However, there are several problems with humming that degrade the retrieval of information. One problem is the human factor. Sometimes, people do not sing accurately, especially if they are inexperienced or unaccompanied. Another problem arises from signal processing. Therefore, a music information retrieval method should be sufficiently robust to surmount various humming errors and signal processing problems. A retrieval system has to extract the pitch from the user's humming. However, pitch extraction is not perfect. It often captures half or double pitches, which are harmonic frequencies of the true pitch, even if the extraction algorithms take the continuity of the pitch into account. Considering these problems, we propose a system that takes multiple pitch candidates into account. In addition to the frequencies of the pitch candidates, the confidence measures obtained from their powers are taken into consideration as well. We also propose the use of an algorithm with three dimensions that is an extension of the conventional Dynamic Programming (DP)algorithm, so that multiple pitch candidates can be treated. Moreover, in the proposed algorithm, DP paths are changed dynamically to take deltaPitches and IOIratios (inter-onset-interval) of input and reference notes into account in order to treat notes being split or unified. We carried out an evaluation experiment to compare the proposed system with a conventional system . When using three-pitch candidates with conference measure and IOI features, the top-ten retrieval accuracy was 94.1%. Thus, the proposed method gave a better retrieval performance than the conventional system.     

Wireless power transfer with unmanned aerial vehicles: State of the art and open challenges

Wireless power transfer (WPT) techniques are emerging as a fundamental component of next-generation energy management in mobile networks. In this context, the use of UAVs opens many possibilities, either using them as mobile energy storage devices to recharge IoT nodes, or to prolong their operation time via smart charging themselves at ground stations. This paper surveys the recent literature on WPT as it applies to UAVs and identifies several open research challenges for the future. As a first step, we tessellate the related research corpus in four fundamental categories (architectures, power and communications enabling technologies, optimization with respect to spatial concepts, optimization of operational aspects). Second, for each category, we provide a critical review of the recent WPT UAV approaches with respect to the way they specialize the general concept of WPT and the extent of their applicability. The survey presents the latest advances in WPT UAV methodologies and related energy-centric services, spanning all the way from the communications aspects deep in the small- and large-scale deployments, up to the operational and applications aspects. Finally, motivated by the rich conclusions of this critical analysis, we identify open challenges for future research. Our approach is horizontal, as the selected publications were drawn from across all vertical areas of research on UAVs. This paper can help the readers to deeply understand how WPT is currently applied to UAVs, and select interesting open research opportunities to pursue.     

Investigating the potential of art neural network models for indexing and information retrieval

Database management systems are very sophisticated, efficient, and fast in information retrieval tasks involving traditional data sets such as numbers, strings, and so on, but many limitations become evident when the data are more complex, that is, high or nondimensional data. Considering some existing problems in information retrieval processes, this work proposes a hybrid system that combines a model of the ART family neural network, ART2‐A, with the Slim‐Tree data structure, which is a metric access method. This approach is an alternative to perform clustering on data in an intelligent way so that the data can be recovered from the corresponding Slim‐Tree. The proposed hybrid system is able to perform range and k‐nearest neighbor queries, which is not an inherent characteristic in implementations involving artificial neural networks. Furthermore, experimental results showed that the performance of the hybrid system was better than the performance of Slim‐Tree. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 319–336, 2007.     

How to place your apps in the fog: State of the art and open challenges

Fog computing aims at extending the cloud towards the Internet of things so to achieve improved quality of service and to empower latency-sensitive and bandwidth-hungry applications. The fog calls for novel models and algorithms to distribute multiservice applications in such a way that data processing occurs wherever it is best placed, based on both functional and nonfunctional requirements. This survey reviews the existing methodologies to solve the application placement problem in the fog, while pursuing three main objectives. First, it offers a comprehensive overview on the currently employed algorithms, on the availability of open-source prototypes and on the size of test use cases. Second, it classifies the literature based on the application and fog infrastructure characteristics that are captured by available models, with a focus on the considered constraints and the optimized metrics. Finally, it identifies some open challenges in application placement in the fog.     

State of the art content based image retrieval techniques using deep learning: a survey

In the recent years the rapid growth of multimedia content makes the image retrieval a challenging research task. Content Based Image Retrieval (CBIR) is a technique which uses features of image to search user required image from large image dataset according to the user’s request in the form of query image. Effective feature representation and similarity measures are very crucial to the retrieval performance of CBIR. The key challenge has been attributed to the well known semantic gap issue. The machine learning has been actively investigated as possible solution to bridge the semantic gap. The recent success of deep learning inspires as a hope for bridging the semantic gap in CBIR. In this paper, we investigate deep learning approach used for CBIR tasks under varied settings from our empirical studies; we find some encouraging conclusions and insights for future research.

     

Learning to embed music and metadata for context-aware music recommendation

Contextual factors greatly influence users’ musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users’ contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users’ historical listening records. Specifically, the proposed approach first learns music pieces’ embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users’ global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user’s preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.     

Toward an understanding of the history and impact of user studies in music information retrieval

Most Music Information Retrieval (MIR) researchers will agree that understanding users’ needs and behaviors is critical for developing a good MIR system. The number of user studies in the MIR domain has been gradually increasing since the early 2000s, reflecting this growing appreciation of the need for empirical studies of users. However, despite the growing number of user studies and the wide recognition of their importance, it is unclear how great their impact has been in the field: on how systems are developed, how evaluation tasks are created, and how MIR system developers in particular understand critical concepts such as music similarity or music mood. In this paper, we present our analysis on the growth, publication and citation patterns, topics, and design of 198 user studies. This is followed by a discussion of a number of issues/challenges in conducting MIR user studies and distributing the research results. We conclude by making recommendations to increase the visibility and impact of user studies in the field.