Evaluation of lens distortion errors in video-based motion analysis

In an effort to study lens distortion errors, a grid of points of known dimensions was constructed and videotaped using a standard and a wide-angle lens. Recorded images were played back on a VCR and stored on a personal computer. Using these stored images, two experiments were conducted. Errors were calculated as the difference in distance from the known coordinates of the points to the calculated coordinates. The purposes of this project were as follows: (1) to develop the methodology to evaluate errors introduced by lens distortion; (2) to quantify and compare errors introduced by use of both a 'standard' and a wide-angle lens; (3) to investigate techniques to minimize lens-induced errors; and (4) to determine the most effective use of calibration points when using a wide-angle lens with a significant amount of distortion. It was seen that when using a wide-angle lens, errors from lens distortion could be as high as 10 percent of the size of the entire field of view. Even with a standard lens, there was a small amount of lens distortion. It was also found that the choice of calibration points influenced the lens distortion error. By properly selecting the calibration points and avoidance of the outermost regions of a wide-angle lens, the error from lens distortion can be kept below approximately 0.5 percent with a standard lens and 1.5 percent with a wide-angle lens

A Discriminative Model for Polyphonic Piano Transcription

We present a discriminative model for polyphonic piano transcription. Support vector machines trained on spectral features are used to classify frame-level note instances. The classifier outputs are temporally constrained via hidden Markov models, and the proposed system is used to transcribe both synthesized and real piano recordings. A frame-level transcription accuracy of 68% was achieved on a newly generated test set, and direct comparisons to previous approaches are provided

Melody Transcription From Music Audio: Approaches and Evaluation

Although the process of analyzing an audio recording of a music performance is complex and difficult even for a human listener, there are limited forms of information that may be tractably extracted and yet still enable interesting applications. We discuss melody--roughly, the part a listener might whistle or hum--as one such reduced descriptor of music audio, and consider how to define it, and what use it might be. We go on to describe the results of full-scale evaluations of melody transcription systems conducted in 2004 and 2005, including an overview of the systems submitted, details of how the evaluations were conducted, and a discussion of the results. For our definition of melody, current systems can achieve around 70% correct transcription at the frame level, including distinguishing between the presence or absence of the melody. Melodies transcribed at this level are readily recognizable, and show promise for practical applications

Participatory Evaluation: An Alternative Strategy for Assessing the Process of Curricular Reform

This paper focuses on the use of participatory evaluation, a relatively new, flexible, interactive approach to assessment, and describes its implementation at two very different postsecondary sites. In particular, the paper addresses the importance of an institution\u27s context in any assessment of curricular reform. Whether diversity was dominant or more marginal in the institution, participatory evaluation allowed it to become an integral part of the process. On both campuses, the voices of students and faculty, from diverse backgrounds, were heard and their suggestions were used. The implementation of participatory evaluation on an urban and a suburban site indicated the approach to be flexible and capable of evolving over time as the project required

A Shift-Invariant Latent Variable Model for Automatic Music Transcription

In this work, a probabilistic model for multiple-instrument automatic music transcription is proposed. The model extends the shift-invariant probabilistic latent component analysis method, which is used for spectrogram factorization. Proposed extensions support the use of multiple spectral templates per pitch and per instrument source, as well as a time-varying pitch contribution for each source. Thus, this method can effectively be used for multiple-instrument automatic transcription. In addition, the shift-invariant aspect of the method can be exploited for detecting tuning changes and frequency modulations, as well as for visualizing pitch content. For note tracking and smoothing, pitch-wise hidden Markov models are used. For training, pitch templates from eight orchestral instruments were extracted, covering their complete note range. The transcription system was tested on multiple-instrument polyphonic recordings from the RWC database, a Disklavier data set, and the MIREX 2007 multi-F0 data set. Results demonstrate that the proposed method outperforms leading approaches from the transcription literature, using several error metrics

MRI diffusion-based filtering: a note on performance characterisation

Frequently MRI data is characterised by a relatively low signal to noise ratio (SNR) or contrast to noise ratio (CNR). When developing automated Computer Assisted Diagnostic (CAD) techniques the errors introduced by the image noise are not acceptable. Thus, to limit these errors, a solution is to filter the data in order to increase the SNR. More importantly, the image filtering technique should be able to reduce the level of noise, but not at the expense of feature preservation. In this paper we detail the implementation of a number of 3D diffusion-based filtering techniques and we analyse their performance when they are applied to a large collection of MR datasets of varying type and quality

Multiple-instrument polyphonic music transcription using a temporally constrained shift-invariant model

A method for automatic transcription of polyphonic music is proposed in this work that models the temporal evolution of musical tones. The model extends the shift-invariant probabilistic latent component analysis method by supporting the use of spectral templates that correspond to sound states such as attack, sustain, and decay. The order of these templates is controlled using hidden Markov model-based temporal constraints. In addition, the model can exploit multiple templates per pitch and instrument source. The shift-invariant aspect of the model makes it suitable for music signals that exhibit frequency modulations or tuning changes. Pitch-wise hidden Markov models are also utilized in a postprocessing step for note tracking. For training, sound state templates were extracted for various orchestral instruments using isolated note samples. The proposed transcription system was tested on multiple-instrument recordings from various datasets. Experimental results show that the proposed model is superior to a non-temporally constrained model and also outperforms various state-of-the-art transcription systems for the same experiment

Improving generalization for polyphonic piano transcription

In this paper, we present methods to improve the generalization capabilities of a classification-based approach to polyphonic piano transcription. Support vector machines trained on spectral features are used to classify frame-level note instances, and the independent classifications are temporally constrained via hidden Markov model post-processing. Semi-supervised learning and multiconditioning are investigated, and transcription results are reported for a compiled set of piano recordings. A reduction in the frame-level transcription error score of 10% was achieved by combining multiconditioning and semi-supervised classification

Characterization of Nannochloropsis Oceanica CCMP1779 grown in light

Nannochloropsis is a genus of fast-growing microalgae that have a high lipid content. Nannochloropsis species have a high triacylglycerol (TAG) content and contain a large amount of the omega-3 long-chain polyunsaturated fatty acid, eicosapentaenoic acid (EPA). There is a growing interest in Nannochloropsis species as models for the study of microalga lipid metabolism and as a platform for synthetic biology. Genome sequences are available for several species, and genetic engineering techniques are being introduced. In this study, I developed a new generation of transgenic vectors for gene stacking and marker-free gene disruption in Nannochloropsis oceanica CCMP1779. These tools enable gene specific studies and were applied to investigate a lipid biosynthetic pathway that is co-expressed under different light conditions. As for all photosynthetic organisms, light plays an important role in driving metabolism and regulation by photosensing in Nannochloropsis species. Each day photosynthetic organisms must maximize their energy capture during the day and be able to sustain themselves during the night. Nannochloropsis cultures synchronize cell division during a light:dark cycle, with cell division occurring at night, along with the usage of stored metabolites that are accumulated during the day. RNA-sequencing measures global transcript abundance, that ultimately might lead to changes in enzymatic activity, metabolism and physiology. I investigated the role of transcriptional regulation on metabolite levels and cell physiology using RNA-sequencing. In the study I found coordination between cell growth, triacylglycerol and hexose content, and transcript abundance of the genes in relevant pathways. Briefly anabolic processes were phased to the light period and catabolic processes phased to the dark period. Furthermore, promoters for transgenic expression were chosen based on transcriptomic measurements gathered in this study. Eicosapentaenoic acid is a high-value fatty acid that is a necessary nutrient for humans, with a biosynthetic pathway consisting of 5 fatty acids desaturases (FADs) and a fatty acid elongase (FAE). Interestingly, the genes of this biosynthetic pathway were strongly co-expressed during light:dark cycles, and I set out to characterize the pathway. Expression of isolated cDNAs in S. cerevisiae resulted in the production of the expected long-chain polyunsaturated fatty acids (LC-PUFAs), and ultimately EPA when all 4 LC-PUFA FADs and an FAE were co-expressed. Selected FADs were overexpressed in N. oceanica and resulted in increased LC-PUFA and EPA content. CRISPR/Cas9 is a potent tool for gene editing. The RNA-guided nuclease, Cas9, was tested as a fusion with green fluorescent protein (GFP) and NanoLuciferase (Nlux) reporters, and the Cas9-Nlux fusion was readily detectable for efficient screening of transformants for recombinant protein production. Single-guide RNAs (sgRNAs) when fused to 5\u2019 and 3\u2019 self-cleaving ribozymes efficiently targeted genes. The two components of the system were expressed from a bidirectional promoter. N. oceanica is capable of expressing transgenes from circular episomal DNA, and an episomal CRISPR construct was generated. The nitrate reductase gene was targeted and the mutants generated with frame-shifts in the coding sequence were unable to grow on nitrate. When antibiotic selection was removed, the episome was lost, and a mutant line that was \u201ccured\u201d of the episome was isolated. These tools are being utilized for gene specific studies in N. oceanica.(Ph. D.)--Michigan State University. Cell and Molecular Biology, 2017Includes bibliographical references (pages 176-201

Identifying "Cover Songs" with Beat-Synchronous Chroma Features

Describes the problem of cover songs, how to calculate chroma features and track beats with dynamic programming, and how to match beat-chroma matrices