C4.5: Programs for Machine Learning (書評)

Perceptual visual quality metrics: A survey

As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.We quantify user engagement both at a per-video (or view) level and a per-user (or viewer) level. In particular, we find that the percentage of time spent in buffering (buffering ratio) has the largest impact on the user engagement across all types of content. However, the magnitude of this impact depends on the content type, with live content being the most impacted. For example, a 1% increase in buffering ratio can reduce user engagement by more than three minutes for a 90-minute live video event. We also see that the average bitrate plays a significantly more important role in the case of live content than VoD content.

/pdf/understanding-the-impact-of-video-quality-on-user-engagement-dbkb3mbmmp.pdf

Understanding the impact of video quality on user engagement

The paper considers the task of recognizing environmental sounds for the understanding of a scene or context surrounding an audio sensor. A variety of features have been proposed for audio recognition, including the popular Mel-frequency cepstral coefficients (MFCCs) which describe the audio spectral shape. Environmental sounds, such as chirpings of insects and sounds of rain which are typically noise-like with a broad flat spectrum, may include strong temporal domain signatures. However, only few temporal-domain features have been developed to characterize such diverse audio signals previously. Here, we perform an empirical feature analysis for audio environment characterization and propose to use the matching pursuit (MP) algorithm to obtain effective time-frequency features. The MP-based method utilizes a dictionary of atoms for feature selection, resulting in a flexible, intuitive and physically interpretable set of features. The MP-based feature is adopted to supplement the MFCC features to yield higher recognition accuracy for environmental sounds. Extensive experiments are conducted to demonstrate the effectiveness of these joint features for unstructured environmental sound classification, including listening tests to study human recognition capabilities. Our recognition system has shown to produce comparable performance as human listeners.

/pdf/environmental-sound-recognition-with-time-frequency-audio-5f5opmb7a5.pdf

Environmental Sound Recognition With Time–Frequency Audio Features

We present our study of audio content analysis for classification and segmentation, in which an audio stream is segmented according to audio type or speaker identity. We propose a robust approach that is capable of classifying and segmenting an audio stream into speech, music, environment sound, and silence. Audio classification is processed in two steps, which makes it suitable for different applications. The first step of the classification is speech and nonspeech discrimination. In this step, a novel algorithm based on K-nearest-neighbor (KNN) and linear spectral pairs-vector quantization (LSP-VQ) is developed. The second step further divides nonspeech class into music, environment sounds, and silence with a rule-based classification scheme. A set of new features such as the noise frame ratio and band periodicity are introduced and discussed in detail. We also develop an unsupervised speaker segmentation algorithm using a novel scheme based on quasi-GMM and LSP correlation analysis. Without a priori knowledge, this algorithm can support the open-set speaker, online speaker modeling and real time segmentation. Experimental results indicate that the proposed algorithms can produce very satisfactory results.

Content analysis for audio classification and segmentation

Exemplary embodiments are directed to wireless power management. A method may include detecting one or more wireless chargers for charging an energy storage device of one or more monitored energy storage devices if the energy storage device drops below a threshold value. Moreover, the method may include selecting a charging scheme for an electronic device associated with the energy storage device.

Power management for electronic devices

The disclosure is directed to techniques for automatic segmentation of a region-of-interest (ROI) video object from a video sequence. ROI object segmentation enables selected ROI or “foreground” objects of a video sequence that may be of interest to a viewer to be extracted from non-ROI or “background” areas of the video sequence. Examples of a ROI object are a human face or a head and shoulder area of a human body. The disclosed techniques include a hybrid technique that combines ROI feature detection, region segmentation, and background subtraction. In this way, the disclosed techniques may provide accurate foreground object generation and low-complexity extraction of the foreground object from the video sequence. A ROI object segmentation system may implement the techniques described herein. In addition, ROI object segmentation may be useful in a wide range of multimedia applications that utilize video sequences, such as video telephony applications and video surveillance applications.

Multi-mode region-of-interest video object segmentation

Automatic discrimination of speech and music is an important tool in many multimedia applications. Previous work has focused on using long-term features such as differential parameters, variances and time-averages of spectral parameters. These classifiers use features estimated over windows of 0.5-5 seconds, and are relatively complex. We present our results of combining the line spectral frequencies (LSFs) and zero crossing-based features for frame-level narrowband speech/music discrimination. Our classification results for different types of music and speech show the good discriminating power of these features. Our classification algorithms operate using only a frame delay of 20 ms, making them suitable for real-time multimedia applications.

/pdf/speech-music-discrimination-for-multimedia-applications-2gg74q73fg.pdf

Speech/music discrimination for multimedia applications

Exemplary embodiments are directed to selective wireless power transfer. A method may include transferring wireless power to at least one electronic device while varying at least one parameter of the wireless power transfer according to a wireless power transfer scenario.

Selective wireless power transfer

The disclosure relates to techniques for video source rate control for video telephony (VT) applications. The source video encoding rate may controlled using a dual-buffer based estimation of a frame budget that defines a number of encoding bits available for a frame of the video. The dual-buffer based estimation technique may track the fullness of a physical video buffer and the fullness of the virtual video buffer. The source video encoding rate is then controlled based on the resulting frame budget. The contents of the virtual buffer depend on constraints imposed by a target encoding rate, while the contents of the physical buffer depend on constraints imposed by varying channel conditions. Consideration of physical video buffer fullness permits the video source rate control technique to be channel-adaptive. Consideration of virtual video buffer fullness permits the video source rate control technique to avoid encoding excessive video that could overwhelm the channel.

Khaled Helmi El-Maleh

Papers

Power management for electronic devices

Multi-mode region-of-interest video object segmentation

Speech/music discrimination for multimedia applications

Selective wireless power transfer

Video source rate control for video telephony