Binaural Cue Coding (BCC) is a method for multichannel spatial rendering based on one down-mixed audio channel and BCC side information. The BCC side information has a low data rate and it is derived from the multichannel encoder input signal. A natural application of BCC is multichannel audio data rate reduction since only a single down-mixed audio channel needs to be transmitted. An alternative BCC scheme for efficient joint transmission of independent source signals supports flexible spatial rendering at the decoder. This paper (Part I) discusses the most relevant binaural perception phenomena exploited by BCC. Based on that, it presents a psychoacoustically motivated approach for designing a BCC analyzer and synthesizer. This leads to a reference implementation for analysis and synthesis of stereophonic audio signals based on a Cochlear Filter Bank. BCC synthesizer implementations based on the FFT are presented as low-complexity alternatives. A subjective audio quality assessment of these implementations shows the robust performance of BCC for critical speech and audio material. Moreover, the results suggest that the performance given by the reference synthesizer is not significantly compromised when using a low-complexity FFT-based synthesizer. The companion paper (Part II) generalizes BCC analysis and synthesis for multichannel audio and proposes complete BCC schemes including quantization and coding. Part II also describes an alternative BCC scheme with flexible rendering capability at the decoder and proposes several applications for both BCC schemes.

Binaural Cue Coding (BCC) is a method for multichannel spatial rendering based on one down-mixed audio channel and side information. The companion paper (Part I) covers the psychoacoustic fundamentals of this method and outlines principles for the design of BCC schemes. The BCC analysis and synthesis methods of Part I are motivated and presented in the framework of stereophonic audio coding. This paper, Part II, generalizes the basic BCC schemes presented in Part I. It includes BCC for multichannel signals and employs an enhanced set of perceptual spatial cues for BCC synthesis. A scheme for multichannel audio coding is presented. Moreover, a modified scheme is derived that allows flexible rendering of the spatial image at the receiver supporting dynamic control. All aspects of complete BCC encoder and decoder implementations are discussed, such as down-mixing of the input signals, low complexity estimation of the spatial cues, and quantization and coding of the side information. Application examples are given and the performance of the coder implementations are evaluated and discussed based on subjective listening test results.

Binaural Cue Coding (BCC) offers a compact parametric representation of auditory spatial information. This representation can be applied to stereophonic or multi-channel audio compression. It allows to reconstruct the spatial image given a mono audio signal and spatial cues that require only a low data volume. This paper focuses on the extraction of the spatial cues from a stereophonic signal with a BCC analyzer. Results of a first subjective quality evaluation confirm that this technique is able to approximate the spatial image of critical reference signals. Thus BCC has the potential to offer highly efficient stereophonic or multi-channel coding, even at very low bit rates that currently permit coding of only one mono channel.

We present a novel concept for representing multi-channel audio signals: Binaural Cue Coding (BCC). BCC aims at separating the basic audio content and the information relevant for spatial perception. A multi-channel audio signal is represented as a mono signal and BCC parameters. We present two types of applications of BCC. Firstly, a number of separate sound source signals are reduced to a mono signal and BCC parameters. In this case, the decoder has control over the location of each source in auditory space. In other words, the decoder can render spatial images as if the separate source signals were given. Secondly, a multi-channel audio signal is reduced to a mono signal and BCC parameters. In this case the decoder generates a multi-channel signal with a spatial image similar to the spatial image of the input signal of the encoder. Results from a subjective test suggest that BCC, combined with existing mono audio coders, offers better quality than conventional stereo and multi-channel perceptual transform audio coders for a wide range of bitrates.

Perceptual audio coders use an estimated masked threshold for the determination of the maximum permissible just-inaudible noise level introduced by quantization. This estimate is derived from a psychoacoustic model mimicking the properties of. masking. Most psychoacoustic models for coding applications use a uniform (equal bandwidth) spectral decomposition as a first step to approximate the frequency selectivity of the human auditory system. However, the equal filter properties of the uniform subbands do not match the nonuniform characteristics of cochlear filters and reduce the precision of psychoacoustic modeling. Even so, uniform filter banks are applied because they are computationally efficient. This paper presents a psychoacoustic model based on an efficient nonuniform cochlear filter bank and a simple masked threshold estimation. The novel filter-bank structure employs cascaded low-order IIR filters and appropriate down-sampling to increase efficiency. The filter responses are optimized for the modeling of auditory masking effects. Results of the new psychoacoustic model applied to audio coding show better performance in terms of bit rate and/or quality of the new model in comparison with other state-of-the-art models using a uniform spectral decomposition. The low delay of the new model is particularly suitable for low-delay coders.

Many applications in auditory modeling require analysis filters that approximate the frequency selectivity given by psychophysical data, e.g., from masking experiments using narrow-band maskers. This frequency selectivity is largely determined by the spectral decomposition process inside the human cochlea. Currently used spectral decomposition schemes for masking modeling in audio coding generally do not achieve the non-uniform time and frequency resolution provided by the cochlea. These applications rather take advantage of the computational efficiency of uniform filter banks or transforms at the expense of coding gain. This paper presents a suitable analysis filter-bank structure employing cascaded low-order IIR filters and appropriate downsampling to increase efficiency. In an application example, the filter responses were optimized to model auditory masking effects. The results show that the time and frequency resolution of the filter bank matches or exceeds the masking properties. Thus, the filter bank enables improved masking modeling for audio coding at low computational costs.