This paper proposes a new method of extracting music boundaries, such as a boundary between musical selections, or a boundary between a musical selection and a speech, for automatic segmentation of \ideo data and other applications. The method utilizes acoustic similarity in a music selection. Similar partial sections are first extracted, by means of a new algorithm called Segmental Continuous Dynamic Programming, or Segmental CDP. The music boundary is identified by reference to multiple similar sections and their location information, as extracted by Segmental CDP. The performance of the proposed method is evaluated for music boundary extraction using actual music data sets. The study demonstrates that the proposed method enables to extract music boundaries well for both evaluation data and a real broadcasted music program.

An adaptive control procedure is proposed for the go-back-N and selective-repeat automatic repeat-request (ARQ) schemes. The procedure is to dynamically vary the number and length of continuously transmitted data blocks according to the bit error rate. The throughput of the adaptive ARQ schemes for a half-duplex transmission line is compared with the throughput of corresponding conventional schemes. Numerical results indicate that adaptive ARQ schemes are superior to conventional ARQ schemes when the bit error rate is not known or varies with time.>