It is foreseen that more and more music objects in symbolic format and multimedia objects, such as audio, video, or lyrics, integrated with symbolic music representation (SMR) will be published and broadcasted via the Internet. The SMRs of the flowing songs or multimedia objects will form a music stream. Many interesting applications based on music streams, such as interactive music tutorials, distance music education, and similar theme searching, make the research of content-based retrieval over music streams much important. We consider multiple queries with error tolerances over music streams and address the issue of approximate matching in this environment. We propose a novel approach to continuously process multiple queries over the music streams for finding all the music segments that are similar to the queries. Our approach is based on the concept of n-grams, and two mechanisms are designed to reduce the heavy computation of approximate matching. One mechanism uses the clustering of query n-grams to prune the query n-grams that are irrelevant to the incoming data n-gram. The other mechanism records the data n-gram that matches a query n-gram as a partial answer and incrementally merges the partial answers of the same query. We implement a prototype system for experiments in which songs in the MIDI format are continuously broadcasted, and the user can specify musical segments as queries to monitor the music streams. Experiment results show the effectiveness and efficiency of the proposed approach.