nils-herrmann/clean_scirepeval huggingface dataset:
Automatic main melody extraction from midi files with a modified Lempel-Ziv algorithm

A dictionary based approach for extracting repetitive patterns in music aimed at music feature extraction and indexing for audio database management is proposed. Segmentation is achieved based on the tempo information and a music score is decomposed into bars. Each bar is indexed and a bar index table is built. Then, an adaptive dictionary based compression algorithm known as Lempel Ziv 78 (LZ-78) is applied to the bar-represented music scores to extract repetitive patterns ( 1978). Finally, pruning is performed to this dictionary to remove non-repeating patterns and combine shorter repeating patterns into a longer repeating pattern. The LZ78 algorithm is slightly modified to achieve better results in the current context. Experiments are performed to MIDI files, and the proposed algorithm has demonstrated an excellent performance.
Current Developments and Future Trends in Audio Authentication

Recent developments in the audio- authentication field include basic, preliminary audio analysis and advanced audio- authentication techniques that exploit audio recording conditions and compressed audio features. Multimedia is involved in every aspect of our lives, and many of us rely on various websites for information on events taking place all over the world. We form opinions based on the content of camera footage, phone conversations, and other recordings. Although some sources give us authentic information, others contain forged content. Concerns regarding how to authenticate multimedia data have led to research in forgery detection, but studies in audio are generally still limited compared to those in image and video.
A ParaBoost Method to Image Quality Assessment

An ensemble method for full-reference image quality assessment (IQA) based on the parallel boosting (ParaBoost) idea is proposed in this paper. We first extract features from existing image quality metrics and train them to form basic image quality scorers (BIQSs). Then, we select additional features to address specific distortion types and train them to construct auxiliary image quality scorers (AIQSs). Both BIQSs and AIQSs are trained on small image subsets of certain distortion types and, as a result, they are weak performers with respect to a wide variety of distortions. Finally, we adopt the ParaBoost framework, which is a statistical scorer selection scheme for support vector regression (SVR), to fuse the scores of BIQSs and AIQSs to evaluate the images containing a wide range of distortion types. This ParaBoost methodology can be easily extended to images of new distortion types. Extensive experiments are conducted to demonstrate the superior performance of the ParaBoost method, which outperforms existing IQA methods by a significant margin. Specifically, the Spearman rank order correlation coefficients (SROCCs) of the ParaBoost method with respect to the LIVE, CSIQ, TID2008, and TID2013 image quality databases are 0.98, 0.97, 0.98, and 0.96, respectively.
Detecting duplicate video based on camera transitional behavior

A fast duplicate video detection system based on camera transitional behavior and the suffix array data structure is proposed in this work. The main idea is to match video clips according to their temporal structures, and frames corresponding to unique events are marked as anchor frames. To simplify the detection process, we use the camera transitional behavior to indicate unique events. Specifically, shot boundaries and the beginning and the ending points of camera panning and tilting movements are detected as anchor frames. The length between neighboring anchor frames is computed to form an one-dimensional sequence, called the gap sequence, which serves as the signature of the video. An efficient gap sequence matching algorithm based on the suffix array data structure is adopted to match two given video signatures. Experiment results show that the proposed framework is not only very fast but also has high accuracy in identifying duplicate video pairs.
3D image quality index using SDP-based binocular perception model

A novel quality index for stereoscopic image pairs is proposed in this work. First, we introduce a parameter called the structural distortion parameter (SDP), which varies according to different distortion types. Then, we use the SDP as a control parameter in a binocular perception model, and apply it into three components of SSIM to obtain an overall quality index. In the proposed framework, the binocular model accommodates distortion types and distortion degrees and, therefore, offers robust quality assessment results for both symmetric and asymmetric distortions. It is shown by experimental results that the proposed index outperforms several existing 3D image quality index methods.
Age/gender classification with whole-component convolutional neural networks (WC-CNN)

A novel Convolutional Neural Network (CNN) solution is presented in this work to achieve human age/gender classification from their facial image data in an unconstrained environment. Our proposed Whole-Component CNN solution contains both the whole face and the facial component networks, and a confidence analysis module to handle the classification task. The proposed WC-CNN solution outperforms prior art in the challenging Adience database by a significant margin.
Enhanced audio source separation in room acoustic environments with selected binaural cues

We study the problem of audio source separation from stereo-channel microphones in a room acoustic environment. Instead of using the whole binaural cues as done in DUET (Degenerate Unmixing Estimation Technique)-type methods, we propose a technique that selects a reliable subset of binaural cues by examining the phase determinacy and the sparse source conditions. We conduct experiments with a simulated room acoustic environment and show a significant performance gain of the proposed technique over the DUET-type methods.
Uncalibrated multiview synthesis based on epipolar geometry approximation

In this work, we propose an efficient multiview rendering algorithm that takes uncalibrated stereo as the input. First, the epipolar geometry of multiple viewpoints is analyzed for multiview display. Then, the camera pose for an arbitrarily selected viewpoint is estimated by algebraic approximation. Finally, by exploiting rectification homographies and disparities of rectified stereo, one can determine multiview images with their estimated camera poses. It is shown by experimental results that the proposed multiview synthesis algorithm can provide well calibrated results without warping distortion.
Downlink SINR analysis for multi-rate W-CDMA systems

The downlink SINR performance for multi-rate W-CDMA systems is derived in this research when the concatenated OVSF/truncated PN code is used. The derivation is complicated by the fact that the auto-correlation term of the concatenated OVSF/truncated PN code is not a random noise. It is shown that this auto-correlation term can be approximated as a binomial random variable. The analytic SINR performance can be obtained based on this approximation, and it matches well with the numerical result.
Rate control of a region-based H.263 video codec under time-varying channels

A region-based video codec based on the H.263+ standard is examined and its associated novel rate control schemes are proposed for visual communication through time-varying low-bit-rate channels. The region-based coding scheme is a hybrid method that consists of the traditional block DCT coding and the object-based coding. Basically, we adopt H.263+ as the platform, and develop a fast macroblock-based segmentation method to implement the region-based video codec. The proposed rate control solution includes rate control in three levels: encoding frame selection, frame-layer rate control and macroblock-layer rate control. The goal is to enhance the visual quality of decoded frames without obvious motion unsmoothness under time-varying channels. The efficiency of the proposed rate control schemes applied to the region-based video codec is demonstrated via several typical test sequences.
Audio content analysis for online audiovisual data segmentation and classification

While current approaches for audiovisual data segmentation and classification are mostly focused on visual cues, audio signals may actually play a more important role in content parsing for many applications. An approach to automatic segmentation and classification of audiovisual data based on audio content analysis is proposed. The audio signal from movies or TV programs is segmented and classified into basic types such as speech, music, song, environmental sound, speech with music background, environmental sound with music background, silence, etc. Simple audio features including the energy function, the average zero-crossing rate, the fundamental frequency, and the spectral peak tracks are extracted to ensure the feasibility of real-time processing. A heuristic rule-based procedure is proposed to segment and classify audio signals and built upon morphological and statistical analysis of the time-varying functions of these audio features. Experimental results show that the proposed scheme achieves an accuracy rate of more than 90% in audio classification.
High-fidelity multichannel audio coding with Karhunen-Loeve transform

A new quality-scalable high-fidelity multichannel audio compression algorithm based on MPEG-2 advanced audio coding (AAC) is presented. The Karhunen-Loeve transform (KLT) is applied to multichannel audio signals in the preprocessing stage to remove interchannel redundancy. Then, signals in decorrelated channels are compressed by a modified AAC main profile encoder. Finally, a channel transmission control mechanism is used to re-organize the bitstream so that the multichannel audio bitstream has a quality scalable property when it is transmitted over a heterogeneous network. Experimental results show that, compared with AAC, the proposed algorithm achieves a better performance while maintaining a similar computational complexity at the regular bit rate of 64 kbit/sec/ch. When the bitstream is transmitted to narrowband end users at a lower bit rate, packets in some channels can be dropped, and slightly degraded, yet full-channel, audio can still be reconstructed in a reasonable fashion without any additional computational cost.
Enhanced Adaptive Frequency Hopping for Wireless Personal Area Networks in a Coexistence Environment

In this paper, we present an enhanced adaptive frequency hopping (EAFH) mechanism for improving the performance of frequency hopping-based wireless personal area networks (WPANs) under frequency-static and frequency-dynamic interference. The proposed mechanism monitors the overall packet error rate (PER) of the system to determine the right number of channels to be excluded from the hopset. Then based on the PER of individual channel, it decides whether to exclude a certain channel or not. Finally, proper packet length is associated with those channels remaining in the hopset. These decisions, which pertain to hopset size and packet length, are made so as to optimize the performance of the hopping system in a coexistence environment. We developed an analytical model to justify the behavior and performance of the proposed mechanism. Simulations are conducted under an environment of some collocated Bluetooth (BT) piconets and a Wi-Fi network to validate the developed model and show the superiority of EAFH. Simulation results show that, compared with those existing mechanisms including orthogonal hopset-based mechanisms, EAFH could provide much higher throughput while still maintaining reasonably good channel occupancy.
Receiver-based congestion control mechanism for Internet video transmission

Efficient transmission of delay-stringent video over the Internet is examined in this work. In our design, an end user adjusts the network load depending on the perceived network status to satisfy the available bandwidth and the individual loss rate requirement while exhibiting network friendly behavior. In particular, we present a model-free, receiver-based congestion control mechanism that bypasses Internet modeling difficulties and yet is capable of feeding back the current network status in accurately and timely manner. Through efficient reaction to network dynamics, the proposed congestion control scheme exhibits fast convergence to the optimal bandwidth, while maintaining fairness with other data streams.
Fading Channel Modeling via Variable-Length Markov Chain Technique

Channel characterization and modeling are essential to the wireless communication system design. A model that optimally represents a fading channel with a variable-length Markov chain (VLMC) is proposed in this paper. A VLMC offers a general class of Markov chains whose structure has a variable order and a parsimonious number of transition probabilities. The proposed model consists of two main components: 1) the optimal fading partition under the constraint of a transmission policy and 2) the derivation of the best VLMC representation with respect to the Kullback-Leibler (K-L) distance of fading samples. The fading partition is used to discretize a continuous fading channel gain. The optimal discretization criterion is developed based on the cost function of fading channel statistics and the transmission policy used in the system. Once a continuous fading channel gain is discretized, a VLMC is then used to model the channel. To obtain the optimal VLMC representation, we use the K-L distance of the discretized fading samples as the optimization criterion. The K-L distance of the discretized fading samples is used to determine the appropriate transition probabilities characterizing the optimal VLMC. Last, we show simulation results that demonstrate the accuracy and the effectiveness of the proposed fading channel representation in modeling the Rayleigh fading as well as the lognormal fading.
Streaming of MPEG-4 speech/audio over Internet

Based on the client-server application model, an MPEG-4 speech/audio streaming system over the Internet is implemented and demonstrated. This system provides an architectural prototype for carriage of MPEG-4 data over the IP (Internet protocol) network via the RTSP (real time streaming protocol) and the RTP (real time protocol). MPEG-4 natural audio parametric coding functionalities are fully explored for the coding of speech signals at 2 kbps to 4 kbps and the coding of non-speech signals at bit rates of 4 kbps up to 16 kbps. A set of MPEG-4 oriented RTSP feedback controls is utilized for client-to-server interactivity.
Efficient block-based intra prediction for image coding with 2D geometrical manipulations

A novel block-based intra-prediction scheme is proposed for efficient image (or intra-frame) coding, where we apply various 2D geometrical manipulations to reference image blocks to enrich the pool of prediction blocks for a given target block. As compared with the traditional line-based intra prediction in H.264/AVC, the new scheme offers a significant coding gain (about 0.24-1.23dB in the PSNR value with the same bit rate) at the cost of higher complexity. Several techniques to reduce the search complexity are also discussed.
Hybrid DCT/wavelet I-frame coding for efficient H.263+ rate control at low bit rates

At low bit rates, the bit budget for I-frame coding in H.263+ can be too high to be practical. A hybrid DCT/wavelet transform based I-frame coding is proposed in this work as a solution to the rate control problem. This new coder is compatible with the H.263+ bit stream syntax, and aims at an R-D optimized performance with a reasonable amount of computational complexity. By employing fast estimation of the coding efficiency with a rate-distortion model and performing an R-D based rate allocation, the hybrid coding scheme achieves higher coding gain at low bit rates.
Image Quality Assessment Using Multi-Method Fusion

A new methodology for objective image quality assessment (IQA) with multi-method fusion (MMF) is presented in this paper. The research is motivated by the observation that there is no single method that can give the best performance in all situations. To achieve MMF, we adopt a regression approach. The new MMF score is set to be the nonlinear combination of scores from multiple methods with suitable weights obtained by a training process. In order to improve the regression results further, we divide distorted images into three to five groups based on the distortion types and perform regression within each group, which is called “context-dependent MMF” (CD-MMF). One task in CD-MMF is to determine the context automatically, which is achieved by a machine learning approach. To further reduce the complexity of MMF, we perform algorithms to select a small subset from the candidate method set. The result is very good even if only three quality assessment methods are included in the fusion process. The proposed MMF method using support vector regression is shown to outperform a large number of existing IQA methods by a significant margin when being tested in six representative databases.
Dynamic call admission control scheme for QoS priority handoff in multimedia cellular systems

A dynamic call admission control (CAC) and its associated resource reservation (RR) schemes are proposed in this research based on the guard channel (GC) concept for a wireless cellular system supporting multiple quality of service (QoS) classes. The proposed CAC policy selects the resource access threshold according to the estimated number of incoming call requests of different QoS classes. The amount of resources to be reserved is dynamically adjusted by considering neighboring-cell higher-priority calls, which are likely to handoff. The rationale behind our proposed dynamic CAC and RR scheme is to make an efficient resource reservation for priority calls by considering potential handoff calls in the neighboring cells, based on their signal to noise ratio (SNR) information and the traffic profile for each mobile. A comprehensive service model is developed, which includes not only mobile terminals' bandwidth requirements but also their different levels of priority, rate adaptivity as well as their mobility. Simulations are conducted by OPNET to study the performance of our proposed scheme in terms of cost function and system utilization under different traffic conditions.
Efficient region-selective subdivision for 3-D meshes

An efficient region-selective subdivision algorithm for 3-D meshes is proposed in this research, The use of subdivision surfaces for constructing and representing 3-D objects has been introduced in computer graphics and related fields. As an interpolating subdivision scheme on triangular meshes, the modified butterfly subdivision (MBS) algorithm is feasible for many applications such as progressive rendering and object shape-preserving. To further improve MBS, we adopt efficient refinement criteria based on the viewing frustum, the surface orientation, the screen-space visibility error and the local mesh flatness. We also provide a set of subdivision rules to model some natural sharp features such as local creases, cusps and darts. By choosing appropriate edges as being sharp, the refined meshes attain desired sharp features in edge regions, while maintaining the same order of smoothness as other subdivision algorithms elsewhere.
Age group classification via structured fusion of uncertainty-driven shape features and selected surface features

In this paper, we present a structured fusion method for facial age group classification. To utilize the structured fusion of shape features and surface features, we introduced the region of certainty (ROC) to not only control the classification accuracy for shape feature based system but also reduce the classification needs on surface feature based system. In the first stage, we design two shape features, which can be used to classify frontal faces with high accuracies. In the second stage, a surface feature is adopted and then selected by a statistical method. The statistical selected surface features combined with a SVM classifier can offer high classification rates. With properly adjusting the ROC by a single non-sensitive parameter, the structured fusion of two stages can provide a performance improvement. In the experiments, we use face images in the public available FG-NET and MORPH databases and partition them into three pre-defined age groups. It is observed that the proposed method offers a correct classification rate of 95.1% in FG-NET and 93.7% in MORPH, which outperforms state-of-the-art methods by a significant margin.
Design of integrated multimedia compression and encryption systems

Two approaches for integrating encryption with multimedia compression systems are studied in this research, i.e., selective encryption and modified entropy coders with multiple statistical models. First, we examine the limitations of selective encryption using cryptanalysis, and provide examples that use selective encryption successfully. Two rules to determine whether selective encryption is suitable for a compression system are concluded. Next, we propose another approach that turns entropy coders into encryption ciphers using multiple statistical models. Two specific encryption schemes are obtained by applying this approach to the Huffman coder and the QM coder. It is shown that security is achieved without sacrificing the compression performance and the computational speed. This modified entropy coding methodology can be applied to most modern compressed audio/video such as MPEG audio, MPEG video, and JPEG/JPEG2000 images.
Distributed edge detection with composite hypothesis test in wireless sensor networks

A statistical approach to distributed edge sensor detection with wireless sensor networks is proposed in this research. The objective is to determine whether a target sensor is located in the edge area of a certain phenomenon or not, based on data from its neighboring sensors. Due to the nature of wireless sensor networks, it is desirable to have a distributed algorithm that has a low computational complexity and a low data communication cost among sensors. With some reasonable assumptions and the aid of composite hypothesis testing, we propose data fusion as well as decision fusion methods for edge sensor detection to fulfill the aforementioned constraints. Numerical experiments are used to demonstrate the efficiency of the proposed algorithm.
Image protection via watermarking on perceptually significant wavelet coefficients

A new scheme to search perceptually significant wavelet coefficients for effective digital watermark casting is proposed in this research. Unlike other watermark casting algorithms, which select a fixed set of DCT or wavelet coefficients in the frequency domain, we use an adaptive method to find significant subbands and a number of coefficients in these subbands. The resulting method is image dependent. Furthermore, the threshold of the selected subband is used as one of the energy weighting factors in the generation of a broadband watermark so that it cannot be easily damaged by frequency-selective filtering, DCT or wavelet based compression attack.
WaveGuide: a joint wavelet-based image representation and description system

Data representation and content description are two basic components required by the management of any image database. A wavelet based system, called the WaveGuide, which integrates these two components in a unified framework, is proposed in this work. In the WaveGuide system, images are compressed with the state-of-the-art wavelet coding technique and indexed with color, texture, and object shape descriptors generated in the wavelet domain during the encoding process. All the content descriptors are extracted by machines automatically with a low computational complexity and stored with a low memory space. Extensive experiments are performed to demonstrate the performance of the new approach.