Abstract: A computer implemented online music distribution system provides for the secure delivery of audio data and related media, including text and images, over a public communications network. The online music distribution system provides security through multiple layers of encryption, and the cryptographic binding of purchased audio data to each specific purchaser. The online music distribution system also provides for previewing of audio data prior to purchase. In one embodiment, the online music distribution system is a client-server system including a content manager, a delivery server, and an HTTP server, communicating with a client system including a Web browser and a media player. The content manager provides for management of media and audio content, and processing of purchase requests. The delivery server provides delivery of the purchased media data. The Web browser and HTTP server provide a communications interface over the public network between the content manager and media players.

Abstract: Data such as a musical track is stored as a secure portable track (SPT) which can be bound to one or more players and can be bound to a particular storage medium, restricting playback of the SPT to the specific players and ensuring that playback is only from the original storage medium. The SPT is bound to a player by encrypting data of the SPT using a storage key which is unique to the player, is difficult to change, and is held in strict secrecy by the player. The SPT is bound to a particular storage medium by including data uniquely identifying the storage medium in a tamper-resistant form, e.g., cryptographically signed. The SPT can also be bound to the storage medium by embedding cryptographic logic circuitry, e.g., integrate circuitry, in the packaging of the storage medium. The SPT is bound by encrypting an encryption key using the embedded logic.

Abstract: Watermark data is encoded in a digitized signal by forming a noise threshold spectrum which represents a maximum amount of imperceptible noise, spread-spectrum chipping the noise threshold spectrum with a relatively endless stream of pseudo-random bits to form a basis signal, dividing the basis signal into segments, and filtering the segments to smooth segment boundaries. The data encoded in the watermark signal is precoded to make the watermark data inversion robust and is convolutional encoded to further increase the likelihood that the watermark data will subsequently be retrievable notwithstanding lossy processing of the watermarked signal.

Abstract: Watermark data is encoded in a digitized signal by forming a noise threshold spectrum which represents a maximum amount of imperceptible noise, spread-spectrum chipping the noise threshold spectrum with a relatively endless stream of pseudo-random bits to form a basis signal, dividing the basis signal into segments, and filtering the segments to smooth segment boundaries. The data encoded in the watermark signal is precoded to make the watermark data inversion robust and is convolutional encoded to further increase the likelihood that the watermark data will subsequently be retrievable notwithstanding lossy processing of the watermarked signal. A watermark alignment module determines which of a large number of offsets of the watermarked data is most likely to correspond to a recognizable watermark. The watermark alignment module uses a single basis signal to evaluate a number of offsets over a relatively narrow range of offsets.

Abstract: A device securely decrypts and writes an encrypted digital file to a local recordable storage medium. The device uses two decryption engines. The first decryption engine incrementally decrypts the encrypted digital file, which is then preprocessed and re-encrypted to form an intermediate file. The second decryption engine then incrementally decrypts the intermediate file and writes the decrypted results to a local recordable storage medium. Both decryption engines perform incremental decryption, such that substantially less than all of the digital file is in decrypted form at any instant. A device in accordance with a second embodiment includes a single decryption engine. The encrypted digital file includes individually encrypted portions, and the decryption engine incrementally decrypts the encrypted portions. These portions are buffered for subsequent writing to the recordable storage medium, but substantially less than all of the individually encrypted portions are stored in decrypted form at any instant.

Abstract: Watermark data is encoded in a digitized signal by forming a noise threshold spectrum which represents a maximum amount of imperceptible noise, spread-spectrum chipping the noise threshold spectrum with a relatively endless stream of pseudo-random bits to form a basis signal, dividing the basis signal into segments, and filtering the segments to smooth segment boundaries. The data encoded in the watermark signal is precoded to make the watermark data inversion robust and is convolutional encoded to further increase the likelihood that the watermark data will subsequently be retrievable notwithstanding lossy processing of the watermarked signal. To produce the endless pseudo-random bit stream, subsequent bits of the sequence are generated in a pseudo-random manner from previous bits of the sequence. The pseudo-random bits are appended to the stream of pseudo-random bits and, additionally, replace a number of bits of the state.

Abstract: An audio encoding system includes quantization, phase matching, compressed domain processing and other improvements enabling high fidelity, low bit rate encoding systems to be formed. In a preferred embodiment a novel transient detector is used to divide an audio source into transient, low frequency non-transient and high frequency non-transient regions. Low frequency non-transients are sinusoid modeled and the residual is low frequency noise modeled. Transients are transform coded and high frequency non-transients and transform coded residual is high frequency noise modeled. The preferred embodiment also includes novel sinusoid, transform coded and noise quantization, among other methods for providing high fidelity data reduction and for interfacing sinusoid modeling and transform coding. Compressed domain time compression and expansion are further achieved with no detrimental effect on transients.

Abstract: Watermark data is encoded in a digitized signal by forming a noise threshold spectrum which represents a maximum amount of imperceptible noise, spread-spectrum chipping the noise threshold spectrum with a relatively endless stream of pseudo-random bits to form a basis signal, dividing the basis signal into segments, and filtering the segments to smooth segment boundaries. The data encoded in the watermark signal is precoded to make the watermark data inversion robust and is convolutional encoded to further increase the likelihood that the watermark data will subsequently be retrievable notwithstanding lossy processing of the watermarked signal. The basis signal fits noise thresholds determined by constant-quality quantization approximation. Noise introduced by quantization is estimated by determining a continuously differentiable function which approximates noise introduced by such quantization and using the function to solve for a relatively optimal gain to be applied during such quantization.

Abstract: Watermark data is encoded in a digitized signal by forming a noise threshold spectrum which represents a maximum amount of imperceptible noise, spread-spectrum chipping the noise threshold spectrum with a relatively endless stream of pseudo-random bits to form a basis signal, dividing the basis signal into segments, and filtering the segments to smooth segment boundaries. The data encoded in the watermark signal is precoded to make the watermark data inversion robust and is convolutional encoded to further increase the likelihood that the watermark data will subsequently be retrievable notwithstanding lossy processing of the watermarked signal. Watermark data is encoded in a basis signal by division of the basis signal into segments and inverting the basis signal in segments corresponding to watermark data bits with a first logical value and not inverting the basis signal in segment corresponding to watermark data bits with a different logical value.

Abstract: Digital products are delivered to a client computer through a wide area network such as the Internet only upon determination that the client computer is located in a geopolitical territory, such as a country or state, for which delivery of the digital product is authorized. A server computer estimates the geopolitical location of the client computer from the client computer's network address through contact information in a network address allocation database. Alternatively, the server computer estimates the geopolitical location of the client computer from the client computer's custom name, e.g., domain name. The domain name itself can specify a country within which the client computer is located. Such can be conventional or can be parse according to ad hoc patterns developed by large, international organizations identified by a root domain name. In addition, contact information for the domain name can be retrieved and geopolitical territory information parsed from the contact information.

Abstract: An adaptive linear predictor is used to predict samples, and residuals from such predictions are encoded using Golomb-Rice encoding. Linear prediction of samples of a signal which represents digitized sound tends to produce relatively low residuals and those residuals tend to be distributed exponentially. Accordingly, linear prediction combined with Golomb-Rice encoding produces particularly good compression rates with very efficient and simple implementation. The accuracy of the linear predictor is improved by including, in the prediction of a current sample of a first channel of the digitized signal, look-ahead sample data from a corresponding second channel of the digitized signal. For example, prediction of a right channel sample of a digitized, stereo, audio signal is improved by inclusion of look-ahead left channel sample data in the right channel sample predictor.

Abstract: An adaptive linear predictor is used to predict samples, and residuals from such predictions are encoded using Golomb-Rice encoding. Linear prediction of samples of a signal which represents digitized sound tends to produce relatively low residuals and those residuals tend to be distributed exponentially. Accordingly, linear prediction combined with Golomb-Rice encoding produces particularly good compression rates with very efficient and simple implementation. A code length used in Golomb-Rice, which is typically referred to as the parameter k, is adapted for each sample in a predictable and repeatable manner to further reduce the size of a Golomb-Rice encoding for each sample. An infinite incident response filter of processed residuals automatically reduces influences of previously processed residuals upon such adaptation as additional samples are processed.

Abstract: An adaptive linear predictor is used to predict samples, and residuals from such predictions are encoded using Golomb-Rice encoding. Linear prediction of samples of a signal which represents digitized sound tends to produce relatively low residuals and those residuals tend to be distributed exponentially. Accordingly, linear prediction combined with Golomb-Rice encoding produces particularly good compression rates with very efficient and simple implementation. A code length used in Golomb-Rice, which is typically referred to as the parameter k, is adapted for each sample in a predictable and repeatable manner to further reduce the size of a Golomb-Rice encoding for each sample. An infinite incident response filter of processed residuals automatically reduces influences of previously processed residuals upon such adaptation as additional samples are processed. The efficiency of Golomb-Rice encoding is improved by limiting the predicted samples to an efficient range.

Abstract: An adaptive predictor is used to predict samples, and residuals from such predictions are encoded using Golomb-Rice encoding to thereby compress a digital signal which includes the samples. The adaptive predictor adapts to residuals between actual and predicted samples at a particular rate. The rate of adaptation is itself adapted periodically to ensure optimum performance of the compression. A number of samples are repeatedly compressed using different adaptation rates, and the adaptation rate which produces the best compression results is used. The adaptation rate can be an exponential power of 2 such that incrementing the adaptation rate effectively doubles the rate at which the predictor is adapted and decrementing the adaptation rate effectively halves the rate at which the predictor is adapted. Accordingly, fewer trials are needed to find a relatively optimal adaptation rate for the predictor.

Abstract: An adaptive linear predictor is used to predict samples, and residuals from such predictions are encoded using Golomb-Rice encoding. Linear prediction of samples of a signal which represents digitized sound tends to produce relatively low residuals and those residuals tend to be distributed exponentially. Accordingly, linear prediction combined with Golomb-Rice encoding produces particularly good compression rates with very efficient and simple implementation. A code length used in Golomb-Rice, which is typically referred to as the parameter k, is adapted for each sample in a predictable and repeatable manner to further reduce the size of a Golomb-Rice encoding for each sample. An infinite incident response filter of processed residuals automatically reduces influences of previously processed residuals upon such adaptation as additional samples are processed.

Abstract: A method and apparatus for generating a summation signal by programmably modulating the intensity levels of a plurality of associated channels of a selection and combining the modulated segments into a unified digital record. A pre-established and user modifiable set of channel intensity levels is provided to the user as a manufacturer recommended modulation and mixing pattern. The user creates, personalizes and subjectively improves sets of channel intensity levels from the basis of default or pre-established patterns and thus produces unique and customized summation signals. A system operator individually adjusts the relative intensity levels of each of a plurality of channels. Each channel is played at default or pre-established intensity, solely and individually, or completely masked, or at a specific relative intensity as programmed by the system operator. Intensity value sets are stored for later access, whereby the operator recreates a previously programmed summation signal.