Compared with image compression, lossless compression algorithms are not nearly as widely used in audio compression. This is changing with the popularity of lossless formats such as FLAC, as people increasingly want to maintain a permanent archive of their audio files. The primary users of lossless compression are audio engineers, audiophiles and those consumers who want to preserve the full quality of their audio files and who disdain the quality loss from lossy compression techniques such as Vorbis and MP3.
First, the vast majority of sound recordings are natural sounds, recorded from the real world, and such data doesn't compress well. In a similar manner, photos compress less efficiently with lossless methods than computer-generated images do. But worse, even computer generated sounds can contain very complicated waveforms that present a challenge to many compression algorithms. This is due to the nature of audio waveforms, which are generally difficult to simplify without a (necessarily lossy) conversion to frequency information, as performed by the human ear.
The second reason is that values of audio samples change very quickly, so generic data compression algorithms don't work well for audio, and strings of consecutive bytes don't generally appear very often. However, convolution with the filter [-1 1] (that is, taking the first difference) tends to slightly whiten (decorrelate, make flat) the spectrum, thereby allowing traditional lossless compression at the encoder to do its job; integration at the decoder restores the original signal. More advanced codecs such as Shorten, FLAC and TTA use linear prediction to estimate the spectrum of the signal. At the encoder, the estimator's inverse is used to whiten the signal by removing spectral peaks while the estimator is used to reconstruct the original signal at the decoder.