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Acoustic-feature-based Musical Instrument Hierarchy and Its Application to Category-level Recognition of Unknown Musical Instruments T ETSURO K ITAHARA, † M ASATAKA G TO †† and H ROSHI G. O KUNO † This paper describes category-level recognition of unknown musical instruments (i.e., mu- sical instruments that are not contained in training data) based on an acoustic-feature-based musical instrument hierarchy. The problem of how the unknown musical instruments should be dealt with is essential in musical instrument recognition. However, this problem has not been dealt with in previous studies.  In this study, we propose category-level recognition of the unknown musical instruments. First, we present a method for automatic acquisition of musical instrument hierarchy, and then, using this hierarchy, we conduct experiments on recognizing the unknown musical instruments at category level. In addition, by introducing a process determining whether musical instruments are known or unknown (i.e., whether the musical instruments are contained in training data or not), we realize flexible musical instrument recognition, that is, individual-instrument-level recognition for known musical in- struments and category-level recognition for unknown musical instruments.  Experimental results showed that around 77% of unknown musical instrument sounds, on average, were correctly recognized at category level. 1. MPEG-7 1),2) 3)～8) 2) 1
LogAt- tackTime  TemporalCentroid  SpectralCentroid 2 3 44 2. 18) 11 3 9)～17)
(1) Σι j = (Σ j +Σ j )/2 (3) 129 129 99% 79 ( 2 ) RWC RWC-MDB-I-2001 20) 50 (ω ?,ω ) 44.1 kHz 16 ω έギω - DM(w ç , w ) DM(ω i ,ω j ) = (µ i − µ j )'Σ i j (µ j 2.1 1 1 1 1 17) 2 2.2 – 1したしてμ έし3 Fig. 1 A musical instrument hierarchy obtained by the proposed method. 4 1 3 1 30 20 10 Fig. 2 A musical instrument hierarchy obtained by the proposed method. 5
3 2 30 20 10
Table 5 Musical instrument categorization at three different levels obtained by Fig. 2.
5 2.3 19 3 130 700 1 4 5 1 1 10 20 30 4
6,247 8 17) 3 19 Table 4   Musical instrument categorization at three different levels obtained by Fig. 1.
1 2 1
2 Conv. Prop. (1) 4 Prop- (2) 5 6 1 5 4. 1 5 17) 4 2.4 . 16) . 15) Martin 12) Eronen Peeters 16) Casey 7) MPEG-7 12) 15) (26) Dubnov 31 1 (21) 18) 19 2.5 23)～25) 7 Table 7   Musical instrument sounds used for recognition
3. 2
7 MIDIしprop.1 3 Fig. 3 Experimental results of recognizing unknown musical instruments at category level. 3.2 4 7 (1) ( 2 ) (1) 3.1し(3) 7 (1) 3.2.1 3 19
1 4 3 4 75 100% 7 2 129 23 18 90% 88% 99%
1 8
ElecPf A ElecPf B SynStr A SynStr B SynBrs A SynBrs B 3.2.2 8 40 23 85% ElecPf A ElecPf A 80 85% 23 86% 85 3.3 23 85% 71% 19 3 6,247 7 2
23 9 Table 9
23)～25) ontology problem mantic mapping problem of se- 27) 1 4. 66.62% 13.16% 77.33% 20.22% 22.67% 3.4 77% 2 (b) 40 9 . .
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) 2002 ) NTT 2001 4 IEA/AIE-2001 Finalist IROS-2001 Best Paper Nomination 14 AAAI ASA IEEE ACM 1996 ditory Scene Analysis" vanced Lisp Technology" 2002 “Computational Au- LEA 1998 “Ad- Francis & Taylor 1998 2001 2000 2003 21
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