Music generation has generally been focused on either creating scores or interpreting them. We discuss differences between these two problems and propose that, in fact, it may be valuable to work in the space of direct performance generation: jointly predicting the notes and also their expressive timing and dynamics. We consider the significance and qualities of the dataset needed for this. Having identified both a problem domain and characteristics of an appropriate dataset, we show an LSTM-based recurrent network model that subjectively performs quite well on this task. Critically, we provide generated examples. We also include feedback from professional composers and musicians about some of these examples.
Perhaps it is precisely because music is so often perceived as a profoundly human endeavour that there has also been, in parallel, an ongoing fascination with automating its creation. This fascination long predates notions such as the Turing test (ostensibly for discriminating automation of the most human behaviour), and has spawned a range of efforts: from attempts at the formalization of unambiguously strict rules of composition to incorporation of complete random chance into scores and performances. The use of rules exemplifies the algorithmic (and largely deterministic) approach to music generation, one that is interesting and outside the scope of the current work; for background on this we refer the reader, for example, to the text by Nierhaus @cite_1 . Our present work, on the other hand, lies in a part of the spectrum that incorporates probability and sampling.
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Music is an expressive form of communication often used to convey emotion in scenarios where "words are not enough". Part of this information lies in the musical composition where well-defined language exists. However, a significant amount of information is added during a performance as the musician interprets the composition. The performer injects expressiveness into the written score through variations of different musical properties such as dynamics and tempo. In this paper, we describe a model that can learn to perform sheet music. Our research concludes that the generated performances are indistinguishable from a human performance, thereby passing a test in the spirit of a "musical Turing test". Internet is being used for several activities by a great range of users. These activities include communication, e-commerce, education, and entertainment. Users are required to register regarding Website in order to enroll Web activities. However, registration can be done by automated hacking software. That software make false enrollments which occupy the resources of the Website by reducing the performance and efficiency of servers, even stop the entire Web service. It is crucial for the Websites to have a system which has the capability of differing human users and computer programs in reading images of text. Completely automated public Turing test to tell computers and human apart (CAPTCHA) is such a defense system against optical character recognition (OCR) software. OCR can be defined as a software which work for defeating CAPTCHA images and make countless number of registration on the Websites. This study focuses on a new method which is splitting CAPTCHA images into several parts with random rotation values, and drawing random lines on a grid background. Lines are in the same color with the CAPTCHA text and they provide a distortion of image with grid background. In this paper, the algorithm of our method is introduced in detail. A common task in dance, martial arts, animation, and many other movement genres is for the character to move in an innovative and yet stylistically consonant fashion. In this paper, we describe two mechanisms for automating this process and evaluate the results with a Turing Test. Our algorithms use the mathematics of chaos to achieve innovation and simple machine-learning techniques to enforce stylistic consonance. Because our goal is stylistic consonance, we used a Turing Test, rather than standard cross-validation-based approaches, to evaluate the results. This test indicated that the novel dance segments generated by these methods are nearing the quality of human-choreographed routines. The test-takers found the human-choreographed pieces to be more aesthetically pleasing than computer-choreographed pieces, but the computer-generated pieces were judged to be equally plausible and not significantly less graceful. Musical or choreographic variation—one of the most fundamental of compositional techniques—are based upon two phases of work: establishment of a grounding theme and a series of structured departures from that theme. Classic examples are Bach's Goldberg Variations for the keyboard or Balanchine's The Four Temperaments ballet. The properties of dynamical chaos provide unusual mechanisms for achieving these effects. The characteristic patterns of strange attractors can be used to capture the underlying theme of the sequence and chaos's sensitive dependence on initial conditions can be used to produce variations. This intriguing notion was proposed by Diana Dabby in the mid-1990s in the context of music and image (1, 2). Inspired by Dabby's work, we set out to apply some similar ideas to dance. The result was the pair of tools that are described in this paper: CHAOGRAPHER, which produces chaotic variations of keyframed movement sequences, and MOTIONMIND, which extracts the patterns from a corpus of movement sequences and uses those patterns to generates original and yet stylistically consonant movement. These two tools, working together, can produce novel, interesting movement. The core of CHAOGRAPHER's strategy for introducing novelty is to create a mapping that associates a sequence of body positions with a chaotic attractor, and then use that mapping to generate variations. The fixed attractor structure guarantees that the variations will resemble the original in a mathematically precise sense, while sensitive dependence on initial conditions guarantees While there is a growing body of work proposing grammars for music, there is little work testing analytical grammars in a generative setting. We explore the process of learning production probabilities for musical grammars from musical corpora and test the results using Kulitta, a recently developed framework for automated composition. To do this, we extend a well-known algorithm for learning production probabilities for context-free grammars (CFGs) to support various musical CFGs as well as an additional category of grammars called probabilistic temporal graph grammars. Musical documents may contain heterogeneous information such as music symbols, text, staff lines, ornaments, annotations, and editorial data. Before any attempt at automatically recognizing the information on scores, it is usually necessary to detect and classify each constituent layer of information into different categories. The greatest obstacle of this classification process is the high heterogeneity among music collections, which makes it difficult to propose methods that can be generalizable to a broad range of sources. In this paper we propose a novel machine learning framework that focuses on extracting the different layers within musical documents by categorizing the image at pixel level. The main advantage of our approach is that it can be used regardless of the type of document provided, as long as training data is available. We illustrate some of the capabilities of the framework by showing examples of common tasks that are frequently performed on images of musical documents, such as binarization, staff-line removal, symbol isolation, and complete layout analysis. All these are tasks for which our approach has shown promising performance. We believe our framework will allow the development of generalizable and scalable automatic music recognition systems, thus facilitating the creation of large-scale browsable and searchable repositories of music documents. A brief background of automated music Musical style and linguistics LISP programming and object orientation Style replication Musical examples Cybernetic composition. A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large corpus of melodies and turned into automated mu- sic composers able to generate new melodies coherent with the style they have been trained on. We employ gated-recurrent unit (GRU) networks that have been shown to be particularly efficient in learning complex sequential activations with arbitrary long time lags. Our model processes rhythm and melody in parallel while modeling the relation between these two properties. Using such an approach, we were able to generate interesting complete melodies or suggest possible continuations of a melody fragment that is coherent with the characteristics of the fragment itself. Machine learning models of music typically break down the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. We explore the use of blocked Gibbs sampling as an analogue to the human approach, and introduce Coconet, a convolutional neural network in the NADE family of generative models. Despite ostensibly sampling from the same distribution as the NADE ancestral sampling procedure, we find that a blocked Gibbs approach significantly improves sample quality. We provide evidence that this is due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from (2014) yields better samples than ancestral sampling. We demonstrate the versatility of our method on unconditioned polyphonic music generation. So far, many of the computational models for rendering music performance have been proposed, but they often consist of many heuristic rules and tend to be complex. It makes difficult to generate and select the useful rules, or perform the optimization of parameters in the rules. In this study, we present a new approach that automatically learns a computational model for rendering music performance with score information as an input and the corresponding real performance data as an output. We use a Gaussian Process (GP) incorporated with a Bayesian Committee Machine to reduce naive GP's heavy computation cost, to learn those input-output relationships. We compared three normalized errors: dynamics, attack time and release time between the real and predicted performance by the trained GP to evaluate our proposed scheme. We evaluated the learning ability and the generalization ability. The results show that the trained GP has an acceptable learning ability for 'known' pieces, but show insufficient generalization ability for 'unknown' pieces, suggesting that the GP can learn the expressive music performance without setting many parameters manually, but the size of the current training dataset is not sufficiently large so as to generalize the training pieces to 'unknown' test pieces. Automated Text Scoring (ATS) provides a cost-effective and consistent alternative to human marking. However, in order to achieve good performance, the predictive features of the system need to be manually engineered by human experts. We introduce a model that forms word representations by learning the extent to which specific words contribute to the text's score. Using Long-Short Term Memory networks to represent the meaning of texts, we demonstrate that a fully automated framework is able to achieve excellent results over similar approaches. In an attempt to make our results more interpretable, and inspired by recent advances in visualizing neural networks, we introduce a novel method for identifying the regions of the text that the model has found more discriminative. With the growing use of Internet and its services, a large number of organizations are making use of it to provide and seek information of the people using those services. This has raised the chances of attacks on such services by interrupting them sending multiple requests to the servers providing these services programmatically. CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) provides a way to differentiate users into humans and bots, or computer programs. This paper includes a new technique that utilizes image from custom mouse cursors and outperforms some most popular CAPTCHA techniques such as Text - based CAPTCHAs and previous Image - based CAPTCHAs. Among the many aspects of human intelligence that currently elude the simulation by machines is that of story understanding. Although many theories of narrative have been proposed, several processes pertaining to narrative remain inadequately formalized and, hence, beyond full mechanization. This work proposes a general formal framework that attempts to make precise such processes and related notions, with first and foremost that of what constitutes a narrative. Emphasis is placed on identifying certain premises that narratives are expected to adhere to, and deriving the formal implications that these have in terms of the computability of the various relevant notions. Among others, it is established that checking whether a discourse is a narrative is decidable, and that narratives can be computably enumerated and, hence, unambiguously indexed. Musical time can be considered to be the product of two time scales: the discrete time intervals of a metrical structure and the continuous time scales of tempo changes and expressive timing (Clarke 1987a). In musical notation both kinds are present, although the notation of continuous time is less developed than that of metric time (often just a word like "rubato" or "accelerando" is notated in the score). In the experimental literature, different ways in which a musician can add continuous timing changes to the metrical score have been identified. There are systematic changes in certain rhythmic forms: for example, shortening triplets (Vos and Handel 1987) and timing differences occurring in voice leading with ensemble playing (Rasch 1979). Deliberate departures from metricality, such as rubato, seem to be used to emphasize musical struc- ture, as exemplified in the phrase-final lengthening principle formalized by Todd (1985). In addition to these effects, which are collectively called expressive timing, there are nonvoluntary effects, such as random timing errors caused by the limits in the accuracy of the motor system (Shaffer 1981) and errors in mental time-keeping processes (Vorberg and Hambuch 1978). These effects are generally rather small - in the order of 10-100 msec. To make sense of most musical styles, it is necessary to separate the discrete and continuous components of musical time. We will call this process of separation quantization, although the term is generally used to reflect only the extraction of a metrical score from a musical performance. Autonomous robots often require domain knowl- edge to act intelligently in their environment. This is particu- larly true for robots that use automated planning techniques, which require symbolic representations of the operating en- vironment and the robot's capabilities. However, the task of specifying domain knowledge by hand is tedious and prone to error. As a result, we aim to automate the process of acquiring general common sense knowledge of objects, relations, and actions, by extracting such information from large amounts of natural language text, written by humans for human readers. We present two methods for knowledge acquisition, requiring only limited human input, which focus on the inference of spatial relations from text. Although our approach is applicable to a range of domains and information, we only consider one type of knowledge here, namely object locations in a kitchen environment. As a proof of concept, we test our approach using an automated planner and show how the addition of common sense knowledge can improve the quality of the generated plans. There has been fascinating work on creating artistic transformations of images by Gatys. This was revolutionary in how we can in some sense alter the 'style' of an image while generally preserving its 'content'. In our work, we present a method for creating new sounds using a similar approach, treating it as a style-transfer problem, starting from a random-noise input signal and iteratively using back-propagation to optimize the sound to conform to filter-outputs from a pre-trained neural architecture of interest. For demonstration, we investigate two different tasks, resulting in bandwidth expansion compression, and timbral transfer from singing voice to musical instruments. A feature of our method is that a single architecture can generate these different audio-style-transfer types using the same set of parameters which otherwise require different complex hand-tuned diverse signal processing pipelines. Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence. Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (, 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation. 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. Current text-to-speech synthesis (TTS) systems are often perceived as lacking expressiveness, limiting the ability to fully convey information. This paper describes initial investigations into improving expressiveness for statistical speech synthesis systems. Rather than using hand-crafted definitions of expressive classes, an unsupervised clustering approach is described which is scalable to large quantities of training data. To incorporate this “expression cluster” information into an HMM-TTS system two approaches are described: cluster questions in the decision tree construction; and average expression speech synthesis (AESS) using cluster-based linear transform adaptation. The performance of the approaches was evaluated on audiobook data in which the reader exhibits a wide range of expressiveness. A subjective listening test showed that synthesising with AESS results in speech that better reflects the expressiveness of human speech than a baseline expression-independent system. Refactoring is a common software development practice and many simple refactorings can be performed automatically by tools. Identifier renaming is a widely performed refactoring activity. With tool support, rename refactorings can rely on the program structure to ensure correctness of the code transformation. Unfortunately, the textual references to the renamed identifier present in the unstructured comment text cannot be formally detected through the syntax of the language, and are thus fragile with respect to identifier renaming. We designed a new rule-based approach to detect fragile comments. Our approach, called Fraco, takes into account the type of identifier, its morphology, the scope of the identifier and the location of comments. We evaluated the approach by comparing its precision and recall against hand-annotated benchmarks created for six target Java systems, and compared the results against the performance of Eclipse's automated in-comment identifier replacement feature. Fraco performed with near-optimal precision and recall on most components of our evaluation data set, and generally outperformed the baseline Eclipse feature. As part of our evaluation, we also noted that more than half of the total number of identifiers in our data set had fragile comments after renaming, which further motivates the need for research on automatic comment refactoring.