An intelligent electronic appliance preferably includes a user interface, data input and/or output port, and an intelligent processor. A preferred embodiment comprises a set top box for interacting with broadband media streams, with an adaptive user interface, content-based media processing and/or media metadata processing, and telecommunications integration. An adaptive user interface models the user, by observation, feedback, and/or explicit input, and presents a user interface and/or executes functions based on the user model. A content-based media processing system analyzes media content, for example audio and video, to understand the content, for example to generate content-descriptive metadata. A media metadata processing system operates on locally or remotely generated metadata to process the media in accordance with the metadata, which may be, for example, an electronic program guide, MPEG 7 data, and/or automatically generated format. A set top box preferably includes digital trick play effects, and incorporated digital rights management features.

Intelligent electronic appliance system and method

An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation which may be efficiently processed to determine correspondence. Applications of the interface and system include a VCR, medical device, vehicle control system, audio device, environmental control system, securities trading terminal, and smart house. The system optionally includes an actuator for effecting the environment of operation, allowing closed-loop feedback operation and automated learning.

Ergonomic man-machine interface incorporating adaptive pattern recognition based control system

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

A software development method and system having a suite of graphical customization tools that enables developers to rapidly configure all aspects of the underlying application software, including the look-and-feel, behavior, and workflow. This is accomplished without modifying application source code, base objects, or SQL. The sophisticated repository management capabilities of the method and system of our invention allows teams of developers to work efficiently on configuring applications. The application upgrader provides an automated process to upgrade the customizations to future product releases thus protecting the investment in customization. The ease, comprehensiveness, scalability, and upgradeability of the customization process help reduce the total lifecycle cost of customizing enterprise applications.

Development tool, method, and system for client server applications

We examine in some detail Mel Frequency Cepstral Coefficients (MFCCs) the dominant features used for speech recognition and investigate their applicability to modeling music. In particular, we examine two of the main assumptions of the process of forming MFCCs: the use of the Mel frequency scale to model the spectra; and the use of the Discrete Cosine Transform (DCT) to decorrelate the Mel-spectral vectors. We examine the first assumption in the context of speech/music discrimination. Our results show that the use of the Mel scale for modeling music is at least not harmful for this problem, although further experimentation is needed to verify that this is the optimal scale in the general case. We investigate the second assumption by examining the basis vectors of the theoretically optimal transform to decorrelate music and speech spectral vectors. Our results demonstrate that the use of the DCT to decorrelate vectors is appropriate for both speech and music spectra. MFCCs for Music Analysis Of all the human generated sounds which influence our lives, speech and music are arguably the most prolific. Speech has received much focused attention and decades of research in this community have led to usable systems and convergence of the features used for speech analysis. In the music community however, although the field of synthesis is very mature, a dominant paradigm has yet to emerge to solve other problems such as music classification or transcription. Consequently, many representations for music have been proposed (e.g. (Martin1998), (Scheirer1997), (Blum1999)). In this paper, we examine some of the assumptions of Mel Frequency Cepstral Coefficients (MFCCs) the dominant features used for speech recognition and examine whether these assumptions are valid for modeling music. MFCCs have been used by other authors to model music and audio sounds (e.g. (Blum1999)). These works however use cepstral features merely because they have been so successful for speech recognition without examining the assumptions made in great detail. MFCCs (e.g. see (Rabiner1993)) are short-term spectral features. They are calculated as follows (the steps and assumptions made are explained in more detail in the full paper): 1. Divide signal into frames. 2. For each frame, obtain the amplitude spectrum. 3. Take the logarithm. 4. Convert to Mel (a perceptually-based) spectrum. 5. Take the discrete cosine transform (DCT). We seek to determine whether this process is suitable for creating features to model music. We examine only steps 4 and 5 since, as explained in the full paper, the other steps are less controversial. Step 4 calculates the log amplitude spectrum on the so-called Mel scale. This transformation emphasizes lower frequencies which are perceptually more meaningful for speech. It is possible however that the Mel scale may not be optimal for music as there may be more information in say higher frequencies. Step 5 takes the DCT of the Mel spectra. For speech, this approximates principal components analysis (PCA) which decorrelates the components of the feature vectors. We investigate whether this transform is valid for music spectra. Mel vs Linear Spectral Modeling To investigate the effect of using the Mel scale, we examine the performance of a simple speech/music discriminator. We use around 3 hours of labeled data from a broadcast news show, divided into 2 hours of training data and 40 minutes of testing data. We convert the data to ‘Mel’ and ‘Linear’ cepstral features and train mixture of Gaussian classifiers for each class. We then classify each segment in the test data using these models. This process is described in more detail in the full paper. We find that for this speech/music classification problem, the results are (statistically) significantly better if Mel-based cepstral features rather than linear-based cepstral features are used. However, whether this is simply because the Mel scale models speech better or because it also models music better is not clear. At worst, we can conclude that using the Mel cepstrum to model music in this speech/music discrimination problem is not harmful. Further tests are needed to verify that the Mel cepstrum is appropriate for modeling music in the general case. Using the DCT to Approximate Principal Components Analysis We additionally investigate the effectiveness of using the DCT to decorrelate Mel spectral features. The mathematically correct way to decorrelate components is to use PCA (or equivalently the KL transform). This transform uses the eigenvalues of the covariance matrix of the data to be modeled as basis vectors. By investigating how closely these vectors approximate cosine functions we can get a feel for how well the DCT approximates PCA. By inspecting the eigenvectors for the Mel log spectra for around 3 hours of speech and 4 hours of music we see that the DCT is an appropriate transform for decorrelating music (and speech) log spectra. Future Work Future work should focus on a more thorough examination the parameters used to generate MFCC features such as the sampling rate of the signal, the frequency scaling (Mel or otherwise) and the number of bins to use when smoothing. Also worthy of investigation is the windowing size and frame rate. Suggested Readings Blum, T, Keislar, D., Wheaton, J. and Wold, E., 1999, Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information, U.S. Patent 5, 918, 223. Martin, K.. 1998, Toward automatic sound source recognition: identifying musical instruments, Proceedings NATO Computational Hearing Advanced Study Institute. Rabiner, L. and Juang, B., 1993, Fundamentals of Speech Recognition, Prentice-Hall. Scheirer, E. and Slaney, M., 1997, Construction and evaluation of a robust multifeature speech/music discriminator, Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing.

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Mel frequency cepstral coefficients for music modeling

A system that performs analysis and comparison of audio data files based upon the content of the data files is presented. The analysis of the audio data produces a set of numeric values (a feature vector) that can be used to classify and rank the similarity between individual audio files typically stored in a multimedia database or on the World Wide Web. The analysis also facilitates the description of user-defined classes of audio files, based on an analysis of a set of audio files that are members of a user-defined class. The system can find sounds within a longer sound, allowing an audio recording to be automatically segmented into a series of shorter audio segments.

Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information

Analysis data are provided which are indicative of plural components making up an original sound waveform. The analysis data are analyzed to obtain a characteristic concerning a predetermined element, and then data indicative of the obtained characteristic is extracted as a sound or musical parameter. The characteristic corresponding to the extracted musical parameter is removed from the analysis data, and the original sound waveform is represented by a combination of the thus-modified analysis data and the musical parameter. These data are stored in a memory. The user can variably control the musical parameter. A characteristic corresponding to the controlled musical parameter is added to the analysis data. In this manner, a sound waveform is synthesized on the basis of the analysis data to which the controlled characteristic has been added. In such a sound synthesis technique of the analysis type, it is allowed to apply free controls to various sound elements such as a formant and a vibrato.

Method of and apparatus for analyzing and synthesizing a sound by extracting and controlling a sound parameter

An apparatus and method for interconnecting a plurality of independent software modules by means of specially configured software objects called "Input" and "Output". The software objects are implemented through library functions which include functions for making an Output object, making an Input object, adding actions to Input objects, making connections between the Input and Output objects of various software modules, sending messages from one software module to another, and receiving messages sent by another software module. The invention also provides a system for interconnecting a plurality of hardware modules, each of which may employ different internal data formats. Additionally, a novel graphical user interface is provided, whereby a user can create, destroy, inspect and/or hide connections between software modules, as well as display data flow between modules.

Apparatus and method for linking software modules

A new media identification system is disclosed. In one aspect, a system may comprise at least one analysis module for receiving and analyzing an received work and generating a corresponding representation thereof; at least one identification (ID) server for receiving the representation from the at least one analysis module and generating a list of unidentifiable received works. A method for identifying new media is disclosed. In one aspect, a method may comprise receiving an unidentified segment; determining whether the unidentified segment is similar to previously received unidentified segments; and sequentially arranging similar unidentified segments in a single super segment.

Method and apparatus for identifying new media content

A method and apparatus for creating a signature of a sampled work in real-time is disclosed herein. Unique signatures of an unknown audio work are created by segmenting a file into segments having predetermined segment and hop sizes. The signature then may be compared against reference signatures. One aspect may be characterized in that the hop size of the sampled work signature is less than the hop size of reference signatures. A method for identifying an unknown audio work is also disclosed.

Erling H. Wold

Papers

Method and article of manufacture for content-based analysis, storage, retrieval, and segmentation of audio information

Method of and apparatus for analyzing and synthesizing a sound by extracting and controlling a sound parameter

Apparatus and method for linking software modules

Method and apparatus for identifying new media content

Method and apparatus for creating a unique audio signature