A comparison of connectionist models of music recognition and human performance

Current artificial neural network or connectionist models of music cognition embody feature-extraction and feature-weighting principles. This paper reports two experiments which seek evidence for similar processes mediating recognition of short musical compositions by musically trained and untrained listeners. The experiments are cast within a pattern recognition framework based on the vision-audition analogue wherein music is considered an auditory pattern consisting of local and global features. Local features such as inter-note interval, and global features such as melodic contour, are derived from a two-dimensional matrix in which music is represented as a series of frequencies plotted over time.Manipulation of inter-note interval affected accuracy and reaction time measures in a discrimination task, whereas the same variables were affected by manipulation of melodic contour in a classification task. Musical training is thought of as a form of practice in musical pattern recognition and, as predicted, accuracy and reaction time measures of musically trained subjects were significantly better than those of untrained subjects. Given the evidence for feature-extraction and weighting processes in music recognition tasks, two connectionist models are discussed. The first is a single-layer perceptron which has been trained to discriminate between compositions according to inter-note interval. A second network, using the back-propagation algorithm and sequential input of patterns, is also discussed.     

An interactive tool to promote musical creativity in people with dementia

Dementia is a growing problem that affects the lives of those diagnosed and caregivers, with symptoms having an effect on memory, communication, the ability to learn new skills and problems with behaviour, such as aggression, agitation and depression. Participation in activities can improve quality of life for people with dementia, reducing behavioural problems and aiding relaxation. Research has established that people with dementia can be both artistically and musically creative, and have an appreciation of music even in the latter stages of the disease. The symptoms of the disease, however, mean that supporting music making activities with this group of people is challenging. This paper describes a prototype system designed to enable people with dementia to create music, using a touch screen interface to control a system which utilises chords to create pleasant-sounding music regardless of any prior musical knowledge. Results of usability studies suggest the system is easy to use, and that pleasant-sounding music can be created with it. Participants, including people with dementia, appeared actively engaged during use of the system, many reporting they enjoyed the experience. Future testing will establish the degree to which people with dementia could be musically creative using such a system.     

一种有效识别MIDI文件中主旋律音轨的方法

互联网上存在着大量的MIDI文件,这使得构建以乐谱信息为存储对象的音乐数据库成为可能。MIDI文件通常由多轨组成,为实现旋律的检索、分析、比较等功能就需要从多轨中提取出主旋律。针对人声音乐,提出一种自动识别MIDI文件中主旋律音轨的方法,该方法先考察MIDI文件的各个音轨,如果该音轨具有旋律特征的片段总长度超过规定阈值,则将其加入到候选音轨集,再通过比较,从中择优选出主旋律音轨。通过测试各种音乐类型的MIDI文件证明了该方法的可行性。     

Aligning music audio with symbolic scores using a hybrid graphical model

We present a new method for establishing an alignment between a polyphonic musical score and a corresponding sampled audio performance. The method uses a graphical model containing both latent discrete variables, corresponding to score position, as well as a latent continuous tempo process. We use a simple data model based only on the pitch content of the audio signal. The data interpretation is defined to be the most likely configuration of the hidden variables, given the data, and we develop computational methodology to identify or approximate this configuration using a variant of dynamic programming involving parametrically represented continuous variables. Experiments are presented on a 55-minute hand-marked orchestral test set. This work supported by NSF grants IIS-0113496 and IIS-0534694. Editor: Gerhard Widmer     

Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries

This article presents a new rule discovery algorithm named PLCG that can find simple, robust partial rule models (sets of classification rules) in complex data where it is difficult or impossible to find models that completely account for all the phenomena of interest. Technically speaking, PLCG is an ensemble learning method that learns multiple models via some standard rule learning algorithm, and then combines these into one final rule set via clustering, generalization, and heuristic rule selection. The algorithm was developed in the context of an interdisciplinary research project that aims at discovering fundamental principles of expressive music performance from large amounts of complex real-world data (specifically, measurements of actual performances by concert pianists). It will be shown that PLCG succeeds in finding some surprisingly simple and robust performance principles, some of which represent truly novel and musically meaningful discoveries. A set of more systematic experiments shows that PLCG usually discovers significantly simpler theories than more direct approaches to rule learning (including the state-of-the-art learning algorithm Ripper), while striking a compromise between coverage and precision. The experiments also show how easy it is to use PLCG as a meta-learning strategy to explore different parts of the space of rule models.     

一种鲁棒的计算机实时伴奏系统

从系统的角度研究计算机自动伴奏问题,构建并实现一个为电子乐器的演奏进行实时伴奏的系统.该系统主要由乐谱跟踪和实时伴奏两个核心模块构成.为了解决乐谱跟踪问题,提出基于扩充窗和重构匹配检测器的动态规划算法,依据该算法能够在实际演奏存在相当错误的情况下实现实际演奏音符在乐谱中的实时定位.为了改善实时伴奏的效果,给出根据乐谱跟踪的定位结果以小节为单位在线加入伴奏音的伴奏策略.实验结果表明,所开发的实时伴奏系统,具有较强的容错能力和良好的实时伴奏效果.     

结合SVD_ TLS及EKF算法的动态自组织模糊神经网络在动态系统中的应用

针对如何优化模糊神经网络的规则及如何合理地调整非线性参数及线性参数等问题,提出了将奇异值分解_总体最小二乘法(SVD_TLS)及扩展卡尔曼滤波(EKF)相结合的动态自组织模糊神经网络(STD_DSFNN)。首先给出了STD DSFNN的结构及各层的含义;其次,用EKF算法学习非线性参数,SVD_TLS算法学习线性参数的同时提取重要模糊规则;最后,通过典型的Machey-Ulass时间序列预测实例验证SVD_TLS及EKF相结合的动态自组织模糊神经网络(STE_DSFNN),同时与DFNN, ANFIS及UKF_DFNN相对比,结果表明STE DSFNN网络结构更紧凑,具有更好的泛化能力。     

A Constraint-based Approach for Annotating Music Scores with Gestural Information

The physical gestures that operate music instruments are responsible for the qualities of the sound being produced in a performance. Gestural information is thereby crucial for a model of music performance, paired with a model of sound synthesis where this information is applied. The highly constrained nature of performers gestures makes this task suitable to be modeled via a constraint-based approach, coupled with a strategy aimed at maximizing the gestural comfort of performers. We illustrate the problem representation, the search strategy and a validation of the model against human performance.     

基于集成学习的音乐识别方法研究

随着信息和多媒体的发展,音乐数据变得更加丰富。如何能够高效地检索和管理音乐数据是一个挑战。音乐 分类是音乐信息检索领域的一个关键问题,可以很好地管理不同类别的音乐数据。基于K-Mcmns聚类的循环静态选择 策略是一种双层选择集成模型,它的第一层是通过基于聚类的选择策略在全部的基分类器中筛选出相互之间差异性较 大的候选基分类器集合,然后通过第二层的循环静态选择策略进行第二轮的选择操作,并进行投票集成,以达到更好的 集成效果。通过两组标准的音乐数据集验证了该策略的有效性。     

Hierarchical music representation for composition and analysis

In this paper, we present intermediate results of continuing research into the utility of generalised hierarchical structures for the representation of musical information. We build on an abstract data type presented in Wigginset al. (1989), usingconstituents, which are structurally significant groupings of musical events. We suggest that a division into such groupings can be musically meaningful, and that it can be more flexible than similar approaches. We demonstrate our representation system at work in both analysis and composition, with output from computer programs. We conclude that it is possible and useful to represent music in a way independent of the particular style, tonal system, etc., of the music itself.The authors work in the Department of Artificial Intelligence, University of Edinburgh, Scotland.     

Automatic background music generation based on actors' mood and motions

Since adding background music and sound effects even to short animations is not simple, an automatic music generation system would help improve the total quality of computer generated animations. This paper describes a prototype system which automatically generates background music and sound effects for existing animations. The inputs to the system are music parameters (mood types and musical motifs) and motion parameters for individual scenes of an animation. Music is generated for each scene. The key for a scene is determined by considering the mood type and its degree, and the key of the previous scene. The melody for a scene is generated from the given motifs and the chord progression for the scene which is determined according to appropriate rules. The harmony accompaniment for a scene is selected based on the mood type. The rhythm accompaniment for a scene is selected based on the mood type and tempo. The sound effects for motions are determined according to the characteristics and intensity of the motions. Both the background music and sound effects are generated so that the transitions between scenes are smooth.     

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.     

Learning from natural instructions

Machine learning is traditionally formalized and investigated as the study of learning concepts and decision functions from labeled examples, requiring a representation that encodes information about the domain of the decision function to be learned. We are interested in providing a way for a human teacher to interact with an automated learner using natural instructions, thus allowing the teacher to communicate the relevant domain expertise to the learner without necessarily knowing anything about the internal representations used in the learning process. In this paper we suggest to view the process of learning a decision function as a natural language lesson interpretation problem, as opposed to learning from labeled examples. This view of machine learning is motivated by human learning processes, in which the learner is given a lesson describing the target concept directly and a few instances exemplifying it. We introduce a learning algorithm for the lesson interpretation problem that receives feedback from its performance on the final task, while learning jointly (1) how to interpret the lesson and (2) how to use this interpretation to do well on the final task. traditional machine learning by focusing on supplying the learner only with information that can be provided by a task expert. We evaluate our approach by applying it to the rules of the solitaire card game. We show that our learning approach can eventually use natural language instructions to learn the target concept and play the game legally. Furthermore, we show that the learned semantic interpreter also generalizes to previously unseen instructions.     

基于冲突指示和分组隐藏节点冲突解析策略

隐藏节点问题是导致IEEE 802.15.4 协议性能下降的一个重要因素,而在IEEE 802.15.4 中没有给出解决该类问题的具体方案.提出一种基于冲突指示和分组的隐藏冲突避免策略（hidden node collision detection and avoidstrategy,简称HNCDAS）,该策略采用分组方法将IEEE 802.15.4 的CAP 周期划分为多个等分时隙,从隐藏冲突导致的部分破损帧中提取出隐藏节点地址信息,依据当前获得的隐藏关系动态地将节点调整到相应的竞争组,竞争组内的节点在同一周期内仍按照二进制后退方法竞争发送消息,不同的竞争组在不同的时隙发送消息,从而彻底解决隐藏冲突问题.与其他隐藏冲突解析策略相比,HNCDAS 具有额外开销少和动态调整等优点.从理论上证明了该策略的收敛性和解析策略时间的上限,实验结果表明,HNCDAS 在数据传递率、吞吐率和能量利用率等方面都有明显的提高.     

基于粗糙集的智能控制方法及拟PID仿真

介绍了粗糙集理论是一种知识发现和数据挖掘的新方法,具有通过系统输入输出数据自动抽取规则的优点。提出了一种新的基于粗糙集的智能控制方法以解决粗糙集在工业控制应用中性能较差的问题。该策略通过采用施加典型测试信号激励控制对象来生成数据以保证数据生成的质量,采用在线分批数据采集并处理的方法实现规则的在线提取和动态调整以保证规则库的完备性,并最终保证了控制的性能。通过一个拟PID控制的仿真实例表明该方法有效。     

Algorithms for musical composition: a question of granularity

Early approaches to computer-generated music are examined, and it is argued that making music is concerned with a higher level of granularity than that of the notes on music paper. Work in artificial intelligence shows that low-level decisions such as the selection of individual notes may actually be subordinate to a model-based control structure, the models being examples of how problems have been resolved. It is suggested that the search for algorithmic rules should be directed by two questions: how to identify units of material of the appropriate granularity, and, given a collection of those units, how to properly assemble them     

Automatic identification of music performers with learning ensembles

This article addresses the problem of identifying the most likely music performer, given a set of performances of the same piece by a number of skilled candidate pianists. We propose a set of very simple features for representing stylistic characteristics of a music performer, introducing ‘norm-based’ features that relate to a kind of ‘average’ performance. A database of piano performances of 22 pianists playing two pieces by Frédéric Chopin is used in the presented experiments. Due to the limitations of the training set size and the characteristics of the input features we propose an ensemble of simple classifiers derived by both subsampling the training set and subsampling the input features. Experiments show that the proposed features are able to quantify the differences between music performers. The proposed ensemble can efficiently cope with multi-class music performer recognition under inter-piece conditions, a difficult musical task, displaying a level of accuracy unlikely to be matched by human listeners (under similar conditions).     

基于核聚类进化算法的音乐情感模糊计算模型

音乐情感计算涉及到多维度多层次结构的复杂情感表征问题,而情感本身所具有的模糊性、细微性和多样性,使得传统的情感识别方法普遍效率低下且正确率不高.为提高识别精度,首先利用高斯径向基函数进行非线性映射,来分辨、提取并放大更多的细节信息.然后通过深入剖析中国古琴乐曲,从中抽取出影响最大的六个情感特征值,并在非线性映射的基础上,构造一种基于核聚类进化算法的音乐情感模糊计算模型.在此基础上,进一步针对算法中统一设定簇半径阈值的不足,提出基于蚁群算法的规则调整策略,并进行系统实验.实验结果表明,与基于概率统计的Beyes分类方法相比,优化后的模糊计算模型具有较好的识别效果.     

Note sequence morphing algorithms for performance of electronic dance music

This paper describes algorithms that can musically augment the realtime performance of electronic dance music by generating new musical material by morphing. Note sequence morphing involves the algorithmic generation of music that smoothly transitions between two existing musical segments. The potential of musical morphing in electronic dance music is outlined and previous research is summarised; including discussions of relevant music theoretic and algorithmic concepts. An outline and explanation is provided of a novel Markov morphing process that uses similarity measures to construct transition matrices. The paper reports on a ‘focus-concert’ study used to evaluate this morphing algorithm and to compare its output with performances from a professional DJ. Discussions of this trial include reflections on some of the aesthetic characteristics of note sequence morphing. The research suggests that the proposed morphing technique could be effectively used in some electronic dance music contexts.