一般社団法人 人工知能学会
Japanese Society for
Artificial Intelligence

人工知能学会研究会資料
JSAI Technical Report
SIG-Challenge-046-05 (11/09)

ۭؒ৘ใΛ༻͍ͨௗͷՎ෼ੳখౡ ྒհ 1 , ਿࢁɹ࣏ 2 , ‫ׯ‬৔ɹޭଠ࿠ 1 , ླ໦ɹྷ࣑ 2 , தᢕ Ұത 1,3Ryosuke KOJIMA1 , Osamu SUGIYAMA2 , Kotaro HOSHIBA1 , Reiji SUZUKI3 , Kazuhiro NAKADAI1,4

1. ౦‫ۀ޻ژ‬େֶ, 2. ‫౎ژ‬େֶ, 3. ໊‫ݹ‬԰େֶ,4. ʢ‫ג‬ʣϗϯμɾϦαʔνɾΠϯεςΟνϡʔτɾδϟύϯ
1. Graduate School of Information Science and Engineering, Tokyo Institute of Technology,
2. Graduate School of Information Science, Nagoya University,
3. Honda Research Institute Japan Co., Ltd.

kojima@cyb.mei.titech.ac.jp, sugiyama@kuhp.kyoto-u.ac.jp,hoshiba@cyb.mei.titech.ac.jp, reiji@nagoya-u.jp, nakadai@jp.honda-ri.com
AbstractຊߘͰ͸ௗͷՎͷࣗಈ෼ੳγεςϜͷߏஙΛ໨తͱ͠ɼͦͷͨΊͷۭؒ৘ใΛߟྀͨ͠Ի‫ݯ‬ಉఆϞσϧఏҊ͢ΔɽௗͷՎΛ෼ੳ͠ɼ͍ͭͲ͜ͰԿͷௗ͕໐͍͍ͯΔ͔Λࣗಈతʹൃ‫͢ݟ‬ΔγεςϜ͸໺ௗ‫ڀݚ‬΍ಈ෺ߦಈֶ‫͍͓ͯʹڀݚ‬ɼ‫؍‬ଌͷޮ཰Խɾେ‫ن‬໛Խͱ͍ͬͨ఺͔Β‫ظ‬଴͞Ε͍ͯΔɽ͔͠͠ɼ͜ͷΑ͏ͳγεςϜ͸ɼҟͳΔ஍఺Ͱಉ࣌ʹෳ਺ͷ໺ௗ͕໐͍͍ͯΔͱ͍ͬͨঢ়‫ʹگ‬ରԠ͢Δඞཁ͕͋Δͱ͍ͬͨࠔ೉͕͋Δɽզʑ͸ɼԻ‫ݕݯ‬ग़ɾఆҐɾ෼཭ɾಉఆͱ͍ͬͨɼϩϘοτௌ֮ͷٕज़Λར༻͢Δ͜ͱͰɼௗͷՎ෼ੳϑϨʔϜϫʔΫͷߏஙΛ໨ࢦ͍ͯ͠ΔɽຊߘͰ͸ɼҐஔ৘ใʹ஫໨͢Δ͜ͱͰɼ͜ΕΒͷ౷߹ΛՄೳʹ͢Δ֬཰Ϟσϧɹ Spatial-Cue-Based

Probabilistic Model (SCBPM) ΛఏҊ͢Δɽ͞ΒʹɼఏҊϞσϧΛ༻͍ͨΞϊςʔγϣϯิॿγεςϜΛߏங͠ɼ࣮σʔλΛ༻͍ͨධՁ࣮‫ݧ‬Λߦͬͨɽͦͷ݁ՌɼௗͷՎࣝผͷλεΫʹ͓͍ͯɼैདྷ๏ΑΓ࠷େͰ 5%ͷࣝผ཰޲্͕֬ೝͰ͖ͨɽ·ͨɼఏҊϞσϧ͕༗ޮͰͳ͍ঢ়‫ʹگ‬ఆٛ͠ɼ‫ڀݚ‬Λߦ͍ͬͯΔɽ͜ΕΒ 5W1H ͷ͏ͪɼ͸͡Ίͷ 4W ͸Ի‫ڥ؀‬தͰͷΠϕϯτʹؔ͢Δॏཁͳ৘ใͰ͋ΔͨΊɼಛʹɼԻ‫ݕݯ‬ग़ʢWhenʣɼԻ‫ݯ‬ఆҐʢWhereʣɼԻ‫ݯ‬෼཭ʢWhatʣɼ࿩ऀೝࣝɾԻ‫ݯ‬ಉఆʢWhoʣͱͯ͠औΓ૊·Ε͍ͯΔɽ͜Ε·Ͱͷ 4W ৘ใநग़͸ͦΕͧΕͷ৘ใʹؔͯ͠‫ݸ‬ผʹநग़͢Δ΋ͷͰ͋Γɼ 4W ͢΂ͯͷ৘ใΛநग़͢Δ୯७ͳख๏͸͜ΕΒΛΧεέʔυతʹ࣮ߦ͢Δ͜ͱͰ͋Δɽͭ·Γɼऩ࿥ͨ͠Ի͔ΒԻ‫ݯ‬Λ‫ݕ‬ग़ɾఆҐ͠ɼෳ਺Ի‫͕͋ݯ‬Δ৔߹ʹ͸ͦΕͧΕ෼཭͠ɼಉఆΛߦ͏ख๏Ͱ͋Δɽ͜ͷΞϓϩʔνʹ͸ೋͭͷܽ఺͕͋ΔɽҰͭ͸ɼॲཧ͕ଟஈʹͳΔͨΊɼ‫͕ࠩޡ‬஝ੵͯ͠͠
·͏͜ͱͰ͋Δɽ΋͏Ұͭ͸ɼԻ‫ݯ‬ͷҐஔͱछྨͷؔ܎ੑͳͲ 4W ؒͷ૬‫ޓ‬ґଘΛߟྀ͍ͯ͠ͳ͍఺Ͱ͋Δɽ͜͏͍ͬͨ՝୊Λѻ͏ͨΊɼԻ‫ݯ‬ఆҐͱԻ‫ݯ‬෼཭Λಉ࣌ʹߦ͏ख๏ͱͯ͠ BNP-MAP ๏ [Otsuka 14] ͕ఏҊ͞Ε͍ͯΔɽ·ͨɼ‫ڥ؀‬ཧղҎ֎Ͱ΋૬‫ʹޓ‬ґଘͨ͠৘ใΛѻ͏Ϟσϧ͸‫͞ڀݚ‬Ε͓ͯΓɼྫ͑͹ɼؔ܎σʔλΛѻ͏Stochastic Block Model ʢSBMʣ ͸ωοτϫʔΫ෼ੳ΍ؔ܎ΫϥελϦϯάͰར༻͞Ε͍ͯΔ [Holland 83]ɽؔ܎
ΫϥελϦϯά͸ɼೋͭҎ্ͷର৅ͷؔ܎Λ༻͍ͯΫϥελϦϯά͢Δख๏Ͱ͋Γɼಉ࣌ʹෳ਺ͷԻ͕ଘࡏ͢Δ৔߹ͷ૬‫ޓ‬ґଘؔ܎Λѻ͏໰୊ͱ΋ؔ܎͕ਂ͍ɽ͓͍ͯ΋ɼैདྷ๏ͱมΘΒͳ͍ੑೳ͕ୡ੒Ͱ͖Δ͜ͱ͕֬ೝͰ͖ͨɽ

1͸͡Ίʹ·ͨɼఆҐͱಉఆͷ૬‫ޓ‬ґଘੑɼಛʹɼ͍ͭͲ͜Ͱ୭͕࿩͍ͯ͠Δ͔ͱ͍͏໰୊͸࿩ऀμΠΞϥΠθʔγϣϯͱͯ͠޿͘‫͞ڀݚ‬Ε͍ͯΔɽྫ͑͹ɼϚΠΫϩϗϯΞϨΠΛ༻͍ͯ౸ୡ࣌ؒࠩΛಛ௃ྔͱͯ͠ิॿతʹར༻͢Δ͜ͱͰԻ‫ڥ؀‬ཧղ͸ɼ‫ڥ؀‬தͷԻʹ஫໨͢Δ͜ͱͰɼը૾ͳͲͷ࿩ऀͷηάϝϯςʔγϣϯਫ਼౓͕޲্Ͱ͖Δ͜ͱ͕஌Β৘ใ͔ΒͰ͸ಘΔ͜ͱ͕೉͍͠৘ใ΍ো֐෺͕ଟ͘ΦΫΕ͍ͯΔ [Pardo 06]ɽ͜ΕΒͷ࿩ऀμΠΞϥΠθʔγϣϯϧʔδϣϯ͕໰୊ͱͳΔΑ͏ͳ‫Ͱڥ؀‬େ͖ͳखॿ͚ͱͳͷख๏͸ձٞࣨͳͲͷࣨ಺Ͱɼਓͷ੠Λର৅ʹ͍ͯ͠Δɽ

Δ͜ͱ͔Βɼ‫ڥ؀‬ϞχλϦϯά΍ϩϘοτͳͲͷ෼໺ͰɼຊߘͰ͸ɼఆҐͱಉఆͷ૬‫ޓ‬ґଘੑΛऔΓѻ͏ͨΊʹɼ஫໨͞Ε͍ͯΔɽԻ‫ڥ؀‬ཧղʹ͓͚Δओͳࠔ೉ͷҰͭ͸ɼௗͷՎ෼ੳΛର৅ʹͨ͠ɽௗͷՎͷԻ‫ݯ‬ಉఆ͸Ի‫ݯ‬ͷҐஔ‫ڥ؀‬தʹ͋Δࠞ߹Իͷத͔Β༗༻ͳ৘ใΛநग़͢Δ͜ͱ৘ใͱਂ݁͘ͼ͍ͭͨλεΫͷҰͭͰ͋ΔɽͳͥͳΒ͹ɼͰ͋Δɽզʑ͸Ի‫͔ڥ؀‬Β 5W1H ৘ใ (When, Where,໺ௗ͸ࣗ෼ͷೄுΓΛ͓࣋ͬͯΓɼۙ͘ͷ‫ݸ‬ମ͸ಉҰͷ‫ݸ‬What, Who, Why, How) Λநग़͢Δ͜ͱΛԻ‫ڥ؀‬ཧղͱମͰ͋Δͱ͍ͬͨ͜ΕΒͷ৘ใΛߟྀͭͭ͠ɼো֐෺ͷ25ଟ͍৿ྛͳͲͷ‫͍͓ͯʹڥ؀‬ɼԻ‫ݯ‬ಉఆΛߦ͏λεΫ͸

2. Ի‫ݯ‬ఆҐ৘ใΛߟྀͨ͠Ϟσϧύϥϝʔλͷֶश๏‫ڥ؀‬ϞχλϦϯάͱͯ͠΋௅ઓతͳλεΫͷҰͭͰ͋Δɽ

3. ෦෼తͳΞϊςʔγϣϯΛߟֶྀͨ͠श๏·ͨɼௗ‫͍͓ͯʹڀݚ‬΋ɼௗͷՎ͸ೄுΓͷओு΍‫ٻ‬ѪͳͲͷ໾ׂΛ࣋ͭ͜ͱ͔Β‫ڵ‬ຯਂ͍ର৅ͷҰͭͰ͋ΔҰͭ໨ͷ՝୊͸ɼ໺ௗ‫؍‬ଌΛߦ͏Α͏ͳ৿ྛ΍԰֎Ͱ

[Catchpole 03]ɽௗͷՎ‫ʹڀݚ‬͸ೋͭͷํ๏͕͋ΓɼҰͭ͸ɼ໦ʑͳͲͷো֐෺΍஍‫ʹܗ‬ΑΔӨ‫ʹڹ‬ΑΓɼԻ‫ݯ‬෼཭͸Α͘ίϯτϩʔϧ͞Εͨ‫Ͱڥ؀‬ͷ‫؍‬ଌͰɼ΋͏Ұͭ͸͕े෼ͳੑೳΛൃ‫ʹͱ͍͜ͳ͖Ͱش‬༝དྷ͍ͯ͠Δɽ࣮ࡍ࣮ࡍͷϑΟʔϧυʹ͓͚Δ‫؍‬ଌͰ͋Δɽલऀ͸৚݅Λίʹ෼཭ԻʹଞԻ‫͔ݯ‬ΒͷԻʢಉ࣌ʹ໐͍͍ͯΔଞͷௗͷϯτϩʔϧͨ͠ௗͷՎͷੑ࣭ͷ‫͕ڀݚ‬ՄೳͰ͋ΓɼෆཁͳՎͳͲʣ͕࿙Εͯ͠·͍ɼࣝผ͕͏·͍͔͘ͳ͍৔߹͕ଟ৚݅Λഉআ࣮ͯ͠‫ݧ‬Λߦ͏͜ͱ͕Ͱ͖ΔɽຊߘͰ͸‫ऀޙ‬͘ଘࡏͨ͠ɽҰํͰɼMUSIC ๏ [Schmidt 86] ΍ͦͷ֦ுʹओ‫؟‬Λ͓͍͓ͯΓɼ͜ͷํ๏Ͱ͸ɼ࣮ࡍͷ‫ڥ؀‬ԼͰௗ͕ख๏͸ɼ԰֎ͷ૽Ի‫ڥ؀‬΍໺ௗ͕ॅॲʹ͢ΔΑ͏ͳ৿ྛͲͷΑ͏ʹ‫׆‬ಈ͍ͯ͠Δ͔Λ௚઀‫؍‬ଌ͢Δ͜ͱ͕Ͱ͖Δ

Ͱ͋ͬͯ΋ɼ͋Δఔ౓ͷԻ‫ݯ‬ͷ౸དྷํ޲ΛਪఆͰ͖Δͨ͜Ίɼ஫໨͞Ε͍ͯΔํ๏Ͱ͋Δɽ

ͱ͕ใࠂ͞Ε͍ͯΔ [দྛ 15, Uemura 15]ɽͦ͜Ͱɼ෼཭

ௗͷՎͷಉఆ͸‫ػ‬ցֶश෼໺Ͱ΋஫໨͞Ε͓ͯΓɼ͍Ի͔Βͷ৘ใΛิॿ͢ΔͨΊʹɼ౸དྷํ޲Λ‫ؚ‬Ίͨ֬཰͔ͭ͘ͷίϯϖςΟγϣϯͰऔΓ্͛ΒΕ͓ͯΓɼ༷ʑͳϞσϧΛఏҊ͢ΔɽఏҊϞσϧ͸ɼ෼཭Իʹؔ͢Δ֬཰෼ख๏͕ఏҊ͞Ε͍ͯΔ [Briggs 13, Goëau 16]ɽ͜ΕΒͷ෍ͱ౸དྷํ޲ʹؔ͢Δ֬཰෼෍ͷ̎ͭͷ෼෍͔Βߏ੒͞ίϯϖςΟγϣϯͰ༻͍ΒΕ͍ͯΔσʔλ͸ 1 ΋͘͠͸ 2ΕΔɽ෼཭Իʹؔ͢Δ෼෍ͱͯ͠͸ɼԻ‫ݯ‬ಉఆͷϞσϧͱνϟϯωϧͷϚΠΫϩϑΥϯͰऩ࿥͞ΕͨԻͰ͋Δɽ3 ͭͯ͠Α͘஌ΒΕ͍ͯΔ ࠞ߹Ψ΢ε෼෍ (GMM: GaussianҎ্ͷϚΠΫϩϑΥϯͰߏ੒͞ΕͨϚΠΫϩϑΥϯΞϨMixture Model) Λ༻͍ɼ౸དྷํ޲ʹؔ͢Δ෼෍ʹ͸ํ޲ΠΛ༻͍ͨऩ࿥͸ɼఆҐ΍෼཭ͷੑೳ޲্ͷͨΊʹௗͷ౷‫Ͱֶܭ‬Α͘༻͍ΒΕΔ von Mises ෼෍Λ༻͍ΔɽՎʹར༻Ͱ͖Δ͜ͱ͕ใࠂ͞Ε͍ͯΔ [Suzuki 16]ɽԻ‫ݯ‬ೋͭ໨͸ɼߏஙͨ͠ϞσϧͷύϥϝʔλͷֶशΛ͍͔ಉఆʹ͍ͭͯ΋ɼఆҐ΍෼཭ͱਂ݁͘ͼ͍͍ͭͯΔͨΊɼʹߦ͏͔ͱ͍͏՝୊Ͱ͋ΔɽఏҊϞσϧͰ͸ɼಉ࣌ࠁͷ෼ϚΠΫϩϑΥϯΞϨΠΛ༻͍Δ͜ͱ͕༗ޮͰ͋Δͱ‫ظ‬଴཭Ի͸ఆҐ৘ใΛ௨ͯ͠૬‫ʹޓ‬ґଘ͍ͯ͠ΔɽఏҊϞσϧͰ͖Δ͕ɼͦͷੑೳධՁʹ͍ͭͯ͸े෼͞Ε͍ͯͳ͍ɽ͸ɼӅΕม਺Λ࣋ͭ֬཰ϞσϧͰ͋ΔͨΊɼExpectationઌߦ‫ڀݚ‬ɹ [খౡ 15] ɹͱͯ͠ɼԻ‫ݯ‬ҐஔΛߟྀͨ͠ಉMaximization (EM) ΞϧΰϦζϜͰֶश͢Δ͜ͱ͕ߟ͑ఆϞσϧ͕ఏҊ͞Ε͍ͯΔɽ͜ͷख๏͸ɼԻ‫ݯ‬ಉఆΛߦ͏ΒΕΔ͕ɼ෼཭Իؒͷ૬‫ޓ‬ґଘੑΛߟֶྀͯ͠श͢ΔඞͨΊͷԻ‫ڹ‬ϞσϧΛ෼཭Ի͝ͱʹಠཱͳϞσϧͱֶͯ͠ཁ͕͋Δɽͦ͜ͰɼຊߘͰ͸৽ͨʹఏҊϞσϧ্Ͱͷ EMशͨ͠‫ޙ‬ɼԻ‫ݯ‬ҐஔΛߟྀͨؔ͠਺Λ༻͍ͯͦΕΒͷϞΞϧΰϦζϜΛಋग़͠ɼ͜ͷ՝୊ͷղܾΛਤΔɽσϧΛ݁߹͢Δͱ͍͏ΞϓϩʔνΛऔ͍ͬͯͨɽ͔͠͠ɼࡾͭ໨ʹσʔλͷΞϊςʔγϣϯʹؔ͢Δ՝୊Ͱ͋Δɽ͜ͷΞϓϩʔν͸ɼ෼཭Ի͝ͱʹಠཱʹֶशΛߦ͏ͨΊɼఏҊϞσϧͷֶशͷͨΊʹ͸Ξϊςʔγϣϯ͕ඞཁͰ͋Ի‫ݯ‬ҐஔΛԻ‫ڹ‬Ϟσϧʹ൓өͰ͖ͳ͔ͬͨɽ͜ΕΛղܾΔ͕ɼ͢΂ͯͷσʔλʹର͠ਓखͰ͜ΕΛߦ͏͸࿑ྗ͕͢ΔͨΊʹɼEM ΞϧΰϦζϜʹΑΔ૬‫ޓ‬ґଘͷֶशΛՄ͔͔Δɽͦ͜ͰɼҰ෦ͷσʔλʹਓखͰΞϊςʔγϣϯΛೳʹͨ͠Ϟσϧ΋ఏҊ͞Ε͍ͯΔ [খౡ 16]ɽ͔͠͠ɼ͜͠ɼ࢒ΓΛਪఆ͢Δͷ͕ଥ౰Ͱ͋Δɽ͢ΔͱɼΞϊςʔͷख๏Ͱ͸Ұ෦ͷύϥϝʔλʹؔ͢ΔύϥϝʔλֶशΛγϣϯͷਪఆ͸େྔͷΞϊςʔγϣϯ͞Ε͍ͯͳ͍σʔλߦ͓ͬͯΒͣɼ·ͨɼ[Kojima 16] Ͱ͸࣮‫ݧ‬΋Ұ‫ڥ؀‬ͷΈͱগ਺ͷΞϊςʔγϣϯσʔλ͔Βࣝผ‫ث‬Λߏ੒͢Δ໰୊ͷ‫ݶ‬ఆతͳ΋ͷͰ͋ͬͨɽຊߘͰ͸ɼ͜ΕΒͷύϥϝʔλͱͳΓɼ͜Ε͸൒‫͋ࢣڭ‬Γֶश໰୊ͱͯ͠஌ΒΕ͍ͯΔɽʹ͍ͭͯͷֶशΛՄೳʹ͠ɼҟͳΔ‫Ͱڥ؀‬ͷ࣮‫ݧ‬Λߦ͍ɼಛʹɼ্ड़ͷ EM ΞϧΰϦζϜͰύϥϝʔλֶशΛ͢ΔϞσϧΛධՁͨ͠ɽ৔߹ʹ͸ɼΞϊςʔγϣϯ͞Ε͍ͯͳ͍σʔλΛܽଛ஋ͱΈͳ͢͜ͱʹΑΓɼࣗવʹ൒‫͋ࢣڭ‬Γֶश΁ͱ֦ு͢Δ2՝୊ͱΞϓϩʔνզʑ͸ɼϚΠΫϩϑΥϯΞϨΠΛ༻͍ͯऩ࿥ͨ͠ௗͷՎΛࣗಈతʹ෼ੳ͠ɼௗͷ‫ڀݚ‬Λิॿ͢ΔγεςϜͷߏங͜ͱ͕Ͱ͖Δ [Nigam 00]ɽ3Χεέʔυ๏Λ໨ࢦ͢ɽຊߘͰ͸Ի‫ݕݯ‬ग़ɾఆҐɾ෼཭ɾಉఆΛλʔ͜͜Ͱ͸ɼऩ࿥ͨ͠Ի͔ΒԻ‫ݯ‬Λ‫ݕ‬ग़ɾఆҐɾ෼཭ɾಉఆήοτʹ͠ɼԻ‫ݯ‬ಉఆʹ͓͍ͯ͜ΕΒΛ౷߹͢ΔϞσϧͷͨΊͷ୯७ͳΧεέʔυख๏Λઆ໌͢Δɽਤ 1 ͸ɼϚ

Spatial-Cue-Based Probabilistic Model (SCBPM) ΛఏҊΠΫϩϑΥϯΞϨΠͰऩ࿥ͨ͠ԻΛ‫ݕ‬ग़ɾఆҐɾ෼཭ɾಉ

͢Δɽఆͷॱʹ࣮ߦͯ͠෼ੳ͢ΔΧεέʔυख๏Λ͍ࣔͯ͠ΔɽϞσϧͷߏஙʹ͋ΓҎԼͷ̏ͭͷ՝୊͕‫͛ڍ‬ΒΕΔɽ͜ͷख๏Ͱ͸ͦΕͧΓͷॲཧ͸ಠཱͯ͠ߦΘΕΔɽ͜ͷઅͷҎ߱Ͱ͸͜ΕΒͷॲཧʹ͍ͭͯͷৄࡉΛड़΂Δɽ

1. Ի‫ݯ‬ఆҐ৘ใΛ༻͍ͨԻ‫ݯ‬ಉఆͷϞσϧԽԻ‫ݕݯ‬ग़ɾఆҐͰ͸ɼԻ‫਺ݯ‬ɼԻ‫ݯ‬ͷҐஔɼԻ‫͕ݯ‬ΞΫ

26

検出・定位分離同定K 個の分離音

K 個のラベル

M ch の収録音cc’ss’d分離音１ਤ 1: Ի‫ݕݯ‬ग़ɾఆҐɾ෼཭ɾಉఆͷΧεέʔυ๏
分離音２๏Λ༻͍ͯୡ੒͞ΕΔɽMUSIC ๏Ͱ͸ͦΕͧΕͷํ޲ θϫʔΛ‫͢ࢉܭ‬Δɽ

周波数 (Hz)時間 (sec)ςΟϒʹͳ͍ͬͯΔ۠ؒͷਪఆΛߦ͏ɽ͜ΕΒ͸ MUSIC͝ͱʹ MUSIC ύϫʔ P (θ) ͱ‫ݺ‬͹ΕΔ৴߸෦෼ۭؒͷύx’

周波数 (Hz)x

時間 (sec)ਤ 2: ఏҊϞσϧͷϕΠδΞϯωοτϫʔΫද‫ݱ‬ʢಉ࣌ࠁͷԻ‫͕ݯ‬ೋͭͷ৔߹ʣ:‫؍‬ଌม਺ xɼx ͸ಉ࣌ࠁͷผͷ෼||aH (θ)a(θ)||

P (θ) = M

H
i=L+1 ||a (θ)ei ||ͨͩ͠ɼa(θ) ͸༧Ί‫ܭ‬ଌ͓ͯ͘͠εςΞϦϯάϕΫτϧɼei ͸ϚΠΫϩϑΥϯΞϨΠͷνϟωϧؒͷ૬ؔߦྻΛ‫ݻ‬཭Ի͔Β‫͞ࢉܭ‬ΕΔԻ‫ڹ‬ಛ௃ྔͰ͋Γɼ‫؍‬ଌม਺ d ͸ͦΕΒͷ౸དྷํ޲ͷϕΫτϧɽԼͷࠞ߹Ψ΢ε෼෍ʹै͏͜ͱͱͳΔɽ༗஋ల։͠ɼ‫ݻ‬༗஋ͷେ͖͍΋ͷॱʹฒ΂ͨ‫ݻ‬༗ϕΫτϧp(x, scj , c) = Ncj (x)p(scj | C = c)p(C = c)ͱͦͷΠϯσοΫε i Ͱ͋Δ. L ͸ MUSIC ๏ͷύϥϝʔλͰ͋ΓɼԻ‫ิީݯ‬ͷ਺ɼM ͸ϚΠΫϩϑΥϯͷ਺Ͱ͋ΔɽMUSIC ๏Ͱ͸৴߸෦෼ۭ͕ؒࡶԻ෦෼ۭؒͱ௚ަ͢Δ͜ͱΛར༻͓ͯ͠ΓɼԻ‫ݯ‬ͷ͋Δํ޲Ͱ P (θ) ͸ߴ͍஋ͨͩ͠ɼscj ͸Ի‫ ݯ‬c ͷ j ൪໨ͷαϒΫϥε (j p(scj |C = c) = 1)ɼN (·) ͸ଟมྔΨ΢ε෼෍Ͱ͋Δɽ͜͜ͰɼԻ‫ݯ‬ͷछྨ C Λ֬཰ม਺ͱ͓͖ɼΞϊςʔγϣϯࡁ

ͱͳΔɽ͜ͷ࣌ɼεϨογϣϧυΛ௒͑Δ΋ͷͷΈΛ‫ݕ‬ग़Έσʔλͷ৔߹ʹ͸‫ݻ‬ఆ஋ͱ͢Δ͜ͱͰɼEM ΞϧΰϦζ͠ɼͦͷϐʔΫ஋Λ‫͚ͭݟ‬Δ͜ͱʹΑΓɼԻ‫ݯ‬ͷํ޲ΛਪϜʹΑΓ൒‫͋ࢣڭ‬ΓֶशΛߦ͏ɽ·ͨɼϞσϧͷߏங‫ޙ‬͸ఆ͢Δɽ·ͨɼ֤࣌ؒϑϨʔϜ͝ͱʹํ޲Λਪఆ͠ɼϑ

MAP(Maximum A Postriori) ਪఆʹΑΓɼԻ‫ݯ‬ͷಉఆΛϨʔϜؒͰͷํ޲ͷ͕ࠩ͋Δᮢ஋ҎԼͰ͋Ε͹ಉҰͷԻߦ͏͜ͱ͕Ͱ͖Δɽ‫ͱݯ‬Έͳ͠ɼ࣌ؒతʹԻ‫ݯ‬Λ௥੻͢Δ͜ͱͰɼԻ‫͕ݯ‬ΞΫςΟϒʹͳ͍ͬͯΔ۠ؒΛਪఆ͢Δ͜ͱ͕Ͱ͖Δɽ͜ΕΒͷεϨογϣϧυͷ஋΋ MUSIC ๏ͷύϥϝʔλͰ͋Δɽ4ఏҊ๏Ի‫ݯ‬෼཭Ͱ͸ࠞ߹Ի͔Β໨తͷԻ‫ݯ‬ͷԻΛநग़͢ΔɽԻલઅͷԻ‫ݯ‬ಉఆͷϞσϧͰ͸Ի‫ݯ‬Ґஔʹؔ͢Δ͜ͱ͸ߟ‫ݯ‬෼཭ͷख๏ͱͯ͠͸ϏʔϜϑΥʔϛϯά๏ͳͲ͕Α͘஌ྀ͞Ε͍ͯͳ͔ͬͨɽ͜͜Ͱ͸Ի‫ݯ‬ҐஔΛར༻Ͱ͖ΔΑΒΕ͍ͯΔɽGHDSS(geometric highorder decorrelation-͏ʹϞσϧΛ֦ு͠ʢ4.1 અʣɼͦͷϞσϧͷύϥϝʔλbased source separation) ๏͸෼཭Իؒͷߴ࣍ແ૬ؔੑΛߟྀͯ͠ϏʔϜϑΥʔϛϯάΛ֦ுͨ͠ख๏Ͱ͋Γɼํ޲ֶशʹ͍ͭͯड़΂Δʢ4.2 અʣɽੑϊΠζʹ‫͍ڧ‬ɽզʑ͸ɼ԰֎ͷ༷ʑͳํ޲ੑϊΠζ͔Β

4.1໺ௗͷՎΛ෼཭͢ΔͨΊɼGHDSS ๏Λ༻͍ͨɽલड़ͷ GMM ʹΑΔԻ‫ڹ‬ϞσϧͰ͸෼཭Ի͝ͱಠཱʹϞఏҊϞσϧԻ‫ݯ‬ಉఆͰ͸ GMM Λ༻͍ͨԻ‫ڹ‬ϞσϧΛར༻͢ΔɽσϧԽ͍ͯͨ͠ɽ͕ͨͬͯ͠ɼ࣌ࠁ tɼ෼཭Ի kt ͝ͱʹಠ͜ͷϞσϧͰ͸ɼҰͭͷԻ‫ݯ‬Ϋϥε͸ෳ਺ͷαϒΫϥεཱͰ͋ͬͨɽఏҊϞσϧ͸֤෼཭ԻؒͷґଘੑΛಋೖ͠ɼΛ࣋ͪɼ֤࣌ࠁͰͷ͋ΔԻ‫͔ݯ‬ΒͷԻ͸ɼͦΕΒͷத͔Β͜ΕΛ֦ுͨ͠΋ͷʹͳ͍ͬͯΔɽఏҊϞσϧͷϕΠδΞ֬཰తʹબ୒͞ΕΔͱԾఆ͢ΔɽΑΓ۩ମతʹ͸ɼप೾਺ϯωοτϫʔΫද‫ݱ‬Λਤ 2 ʹࣔ͢ɽ͜͜Ͱɼ࣌ࠁ t ͷԻεϖΫτϧ͔Β‫ͨ͠ࢉܭ‬Ի‫ڹ‬ಛ௃ྔ͕ଟมྔΨ΢ε෼෍‫ ݯ‬kt ͷํ޲ dt = dt,1 , dt,2 , ..., dt,kt , ..., dt,Kt (0 ≤ dt,kt <ʹै͏ͱ͠ɼҰͭͷԻ‫ݯ‬ΫϥεͰ͋ͬͯ΋αϒΫϥεͷ਺

2π, 1 ≤ kt ≤ Kt ) ͸ MUSIC ๏Λ༻͍ͯ‫͖Ͱࢉܭ‬Δɽ͚ͩ͜प೾਺εϖΫτϧͷύλʔϯΛද‫͖Ͱݱ‬Δɽຊ‫ڀݚ‬ͷ࣌ɼ3 અͰड़΂ͨΑ͏ʹ࣌ࠁ t ͷԻ‫ ਺ݯ‬Kt ΋ܾఆ͢Δ.

Ͱ͸ɼप೾਺εϖΫτϧΛ࣌ؒํ޲ʹओ੒෼෼ੳʢPCAʣ

·ͨɼ֤෼཭ԻͷԻ‫ڹ‬ಛ௃ྔ xkt ͸ GHDSS ๏Λ༻͍ͯʹΑΓ࣍‫ݩ‬ѹॖΛͨ͠ϕΫτϧΛԻ‫ڹ‬ಛ௃ྔͱͨ͠ɽ‫͖Ͱࢉܭ‬ΔɽͦͷͨΊɼਤ 2 Ͱ͸͜ΕΒΛ‫؍‬ଌͱͯ͠ද͜ͷΑ͏ʹϞσϧԽ͢ΔͱɼೖྗͷԻ‫ڹ‬ಛ௃ྔ x ͸Ҏ‫͍ͯ͠ݱ‬ΔɽҎԼͰ͸ɼ࣌ࠁ t ͸؆୯ͷͨΊʹ

27ͨͩ͠ɼst,kt ͸࣌ࠁ t ͷԻ‫ ݯ‬kt ʹؔ͢ΔαϒΫϥεΛද۩ମతʹ͸ɼҎԼͷΑ͏ʹ‫ه‬ड़͞ΕΔɽ

p(x, d, s, c) =p(d | c)

K

k=1

Nsck (xk )p(sck | ck )p(ck )(1)p(d | c) =֬͢཰ม਺ͱ͠ɼX ͸࣌ࠁ t ͷԻ‫ڹ‬ಛ௃ྔશͯͷू߹ͱ͖ͳ͍ɽ۩ମతʹɼp(st,kt = s, X, d) Λ‫͢ࢉܭ‬Δํ๏Λ

ci =cj ,i=jp(di , dj | ci = cj )ͷੑ࣭͔Βɼp(st,kt = s, X, d) ͸Ի‫ ݯ‬kt ͚ͩͰͳ͘ɼ࣌
ࠁ t ʹ͓͚Δͦͷ΄͔ͷԻ‫ͱݯ‬ಠཱʹܾఆ͢Δ͜ͱ͸Ͱp(di , dj | ci = cj )͢Δ. p(st,kt = s, X, d) ͸ఏҊϞσϧ্Ͱ‫͢ࢉܭ‬Δ͜ͱ͕Ͱ͖Δɽ ͨͩ͠ɼਤ 2 ΑΓɼϕΠδΞϯωοτϫʔΫ(2)ࣔ͢ɽ·ͣɼ͜͜Ͱ͸؆୯ͷͨΊ࣌ࠁ t ʹ 2 ͭͷԻ‫ݯ‬ͷΈ͕͋Δͱͯ͠ɼͦΕͧΕɼԻ‫ ݯ‬kt ɼkt ɼԻ‫ڹ‬ಛ௃ྔ x,ci =cj ,i=jp(di , dj | ci = cj ) =f (di − dj ; κ1 )(3)

ͱ xʢX ʹ {x, x }ʣɼԻ‫ ޲ํݯ‬d ͱ d ͕༩͑ΒΕͨ৔߹p(di , dj | ci = cj ) =f (di − dj + π; κ2 )(4)Λߟ͑Δɽ͢ΔͱɼԻ‫ ݯ‬kt ͷαϒΫϥε s ʹؔ͢Δ֬཰
p(s, X, d) ͸ҎԼͷΑ͏ʹද‫͞ݱ‬ΕΔɽ

f (d; κ) =exp(κ cos(d))
2πI0 (κ)(5)
p(s, X, d)=p(d, d |c, c )p(x|s)p(s|c)p(c)p(x |c )p(c )ͨͩ͠ɼ f (d; κ) ͸ von Mises ෼෍Ͱ͋ΓɼI0 (κ) ͸ 0࣍ͷม‫ܗ‬ϕοηϧؔ਺Ͱ͋Δɽ·ͨɼκ ͸෼෍ͷूத౓Λද͢ύϥϝʔλͰ͋Δ (κ ≥ 0)ɽࣜ (3) Ͱఆٛ͞ΕΔp(di , dj | ci = cj ) ɹʹ஫໨͢Δͱɼ͜ͷ֬཰஋͸ೋͭͷԻ‫ݯ‬ͷҐஔ͕ۙ͘ɼ͔ͭɼೋͭͷԻ‫͕ݯ‬ಉ͡Ϋϥεʹଐ͠(8)c,cͨͩ͠ɼ p(x |c ) =  s p(x |s )p(s |c )p(c ) ͱ͢ΔɽೋͭҎ্ͷԻ‫͕͋ݯ‬Δ৔߹ɼp(x|c) ΛԿ౓΋‫͢ࢉܭ‬Δඞཁ͕͍͋ͯΔ࣌ʹߴ͍஋ΛͱΔɽҰํɼࣜ (4) Ͱఆٛ͞ΕΔɹΔͨΊɼ༧Ίґଘ͍ͯ͠ΔϑϨʔϜશͯʹରͯ͠ p(x|c)

p(di , dj | ci = cj ) ʹ஫໨͢Δͱɼ͜ͷ֬཰஋͸ೋͭͷԻ‫ݯ‬ͷҐஔ͕ԕ͘ɼ͔ͭɼೋͭͷԻ‫͕ݯ‬ҟͳΔΫϥεʹଐ͠Λ‫͠ࢉܭ‬ɼςʔϒϧΛ࡞͓ͬͯ͘͜ͱͰɼߴ଎ʹ‫͢ࢉܭ‬Δ͜ͱ͕Ͱ͖Δɽ·ͨɼ p(s|x) ͸ s ʹؔ͢ΔଟมྔΨ΢ε͍ͯΔ࣌ʹߴ͍஋ΛͱΔɽಉ࣌ࠁʹೋͭҎ্ͷԻ‫͕͋ݯ‬෼෍ͱͳΓɼͦΕҎ֎ͷ֬཰͸ఆٛΑΓ༩͑ΒΕΔɽΔ৔߹ (Kt > 2) Λߟྀ͢ΔͨΊʹɼp(d | c) ͸ࣜ (2) ͷvon Mises ෼෍ͷύϥϝʔλ κ1 , κ2 ʹ͍ͭͯ΋ EM ΞϧΰϦζϜͰܾఆՄೳͰ͋Δɽ͜ΕΒͷύϥϝʔλͷߋ৽Α͏ʹ͢΂ͯͷԻ‫ؒݯ‬ͷ૊Έ߹ΘͤʹΑͬͯఆٛ͞Εͯࣜ͸ɼࠞ߹ von Mises ෼෍ͷύϥϝʔλਪఆ [Banerjee 05]

͍Δɽ͜ͷϞσϧΛ༻͍ͯԻ‫ݯ‬ͷΫϥεΛਪఆ͢Δͱ͖ʹ͸ɼಉ࣌ࠁͷଞͷԻ‫ݯ‬ͷΫϥεΛߟྀ͢Δඞཁ͕͋Δɽͨ͠(new)ͱྨࣅͨ͠΋ͷʹͳΔɽκ1 ͷߋ৽஋ κ1ҎԼͷΑ͏ʹͳΔɽ͕ͬͯɼԻ‫ݯ‬Ϋϥεͷ MAP ਪఆ͸ҎԼͷΑ͏ʹͳΔɽc∗ = argmax p(x | c)p(d | c)p(c)c4.2

Uc=c = t(6)Vc=c =(new)આ໌͢Δɽ·ͣɼԻ‫ڹ‬ಛ௃ྔ x ʹରԠ͢ΔΫϥε c ͕༩͑ΒΕͨ৔߹ɼͭ·Γ‫͋ࢣڭ‬Γֶशͷ৔߹ɼਤ 2 ΑΓɼϕΠδΞϯωοτϫʔΫͷੑ࣭͔Βɼc ͸ଞͷԻ‫ݯ‬Ϋϥε c ͱಠཱʹ‫͢ࢉܭ‬Δ͜ͱ͕Ͱ͖ɼैདྷͷ GMM ʹΑΔԻ‫ڹ‬Ϟ

κ1Uc=c
Vc=cɹΠϕϯτʹ͍ͭͯ c = c , x = x Մೳͳ૊Έ߹ΘͤΛ‫ࢉܭ‬
͢ΔɽA(x) ͸ҎԼͷΑ͏ʹఆٛ͞ΕΔɽ

A(x) =͔͠͠ɼ෦෼తͳΞϊςʔγϣϯͷ৔߹ɼͭ·Γɼ൒‫ࢣڭ‬ͱʹಠཱʹֶश͢Δ͜ͱ΋Ͱ͖ͳ͍ɽҎԼͰ͸Ϋϥε cɼ

x,x c=cp(c | d, xt )p(c | d , xt ) ɹ͜͜ͰɼUc=c ͱ Vc=c ͸Ϟσϧ্Ͱ͢΂ͯͷಉ࣌ࠁͷԻσϧͷύϥϝʔλֶशͱಉ༷ʹֶशΛߦ͏͜ͱ͕Ͱ͖Δɽ͋ΓֶशΛߦ͏৔߹ʹ͸ɼc ͱ c ͕ಠཱͱ͸ͳΒͣɼx ͝=A−1cos(d − d )p(c | d, xt )p(c | d , xt ) ɹx,x c=c tఏҊϞσϧͷύϥϝʔλֶशຊઅͰ͸ఏҊϞσϧʹ͓͚Δ EM ΞϧΰϦζϜʹ͍ͭͯΛܾΊΔࣜ͸

I1 (x)I0 (x)

ͨͩ͠ɼI0 (x) ͱ I1 (x) ͸ͦΕͧΕ 0 ࣍ͱ 1 ࣍ͷม‫ܗ‬ϕοc ͕Ξϊςʔγϣϯ͞Ε͍ͯͳ͍৔߹ʹ͍ͭͯઆ໌͢Δɽ

ηϧؔ਺ͱ͢Δ. A(x) ͷ‫ ਺ؔٯ‬A−1 (x) ͸ҎԼͷۙࣅࣜEM ΞϧΰϦζϜʹ͓͍ͯ͸ɼσʔληοτதͷαϒΫϥε s ͷग़‫཰֬ݱ‬ͷ‫ظ‬଴஋Λ‫͢ࢉܭ‬Δඞཁ͕͋ΓɼҎԼͷͰ‫ࢉܭ‬ՄೳͰ͋Δ [Sra 12]ɽA−1 (x) ≈Α͏ʹද‫͞ݱ‬ΕΔɽNs =tktp(st,kt = s, X, d)(7)x(2 − x2 )1 − x2κ2 ͷߋ৽ࣜ΋ c = c Λ c = c ͱ͢Δ͜ͱʹΑΓɼಉ༷ʹ‫ࢉܭ‬ՄೳͰ͋Δɽ

28

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K 個のラベルਤ 4: ऩ࿥γεςϜද 1: σʔληοτ (A): ϥϕϧͱΠϕϯτͷ਺ɼ ৭͸ਤ収録

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redcyan
blue

13
17

yellowgreenϓϩτλΠϓγεςϜ

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Δ͜ͱͰɼ 54 ͷΠϕϯτ͕நग़͞Εͨʢਤ 6ʣɽ͜ͷ࣌ɼ

MUSIC ๏ͷύϥϝʔλ͸ɼҰͭͷΠϕϯτ͕ௗͷՎͷҰϑϨʔζʹͳΔΑ͏ʹબ୒ͨ͠ɽ͜ΕΒͷΠϕϯτʹର͠ɼ෼཭ԻΛखֻ͔Γʹɼද 1 ͷϥϕϧΛ༻͍ͯɼਓखͰΞϊςʔγϣϯΛߦͬͨ΋ͷΛਖ਼ղͱͯ͠ɼσʔληοτΛ࡞੒ͨ͠ɽσʔληοτ (B) ͸ 2013 ೥ 5 ݄ 9 ೔ͷேʢ੖ఱʣʹΞϝϦΧͷΧϦϑΥϧχΞभͷ਑༿थͱφϥͷࠞ߹थྛͰऩ࿥ͨ͠ɽऩ࿥ͨ͠ 4 ෼ؒʹ 140 ͷΠϕϯτΛநग़ͨ͠ʢਤ 7 ʣɽσʔληοτ͸ද 2 ʹࣔͨ͠ 8 ͭͷϥϕϧΛ༻͍ͨɽ

͚ͭΔͱ࢒Γͷϥϕϧʹࣗಈతʹϥϕϧͷީิ͕൒ಁ໌Ͱண৭͞ΕΔΑ͏ʹͳ͓ͬͯΓɼΞϊςʔλ͸༧ଌ͞Εͨϥϕϧ͕ਖ਼͍͔͠Ͳ͏͔Λ֬ೝɾमਖ਼͢Δ͜ͱ͕Ͱ͖ΔΑ͏ʹͳ͍ͬͯΔɽ

65712ධՁ࣮‫ݧ‬

ਤ 5 ͱ ਤ 8 ͸ͦΕͧΕσʔληοτ (A)(B) ʹؔͯ͠ɼΧεέʔυ๏ͱ SCBPM Λ༻͍ͨఏҊγεςϜͰͷ݁ՌΛൺֱͨ͠΋ͷͰ͋Δɽ͜Ε͸ɼऩ࿥࣌ؒΛ 10 ౳෼͠ɼϥϕϧ͋Γͷ۠ؒͱϥϕϧͳ͠ͷ۠ؒʹ෼͚ͯɼϥϕϧͳ͠ͷ۠ؒͷΠϕϯτΛ༧ଌͨ͠ࡍͷਖ਼౴཰ͰධՁΛߦͬͨɽ͜ΕΒͷάϥϑͷԣ࣠ʢΞϊςʔγϣϯ཰ʣ͸શମͷ

SCBPM ΛධՁ͢ΔͨΊʹɼσʔληοτ (A)(B) ͷೋͭ͏ͪͷϥϕϧ͋Γͷ۠ؒͷׂ߹Ͱ͋ΔɽͷҟͳΔσʔληοτΛ༻ҙͨ͠ɽ
1 Honda Research Institute Japan Audition for Robots with Ky-oto University
2 http://www.dev-audio.com/͜͜Ͱ༻͍ͨɼԻ‫ڹ‬ಛ௃ྔɾϞσϧύϥϝʔλ͸ҎԼͷΑ͏ʹઃఆͨ͠ɽ·ͣɼ16-bit 16kHz αϯϓϦϯάͷ෼཭Ի͔Β૭෯ 80ʢΦʔόʔϥοϓ͸ 40 αϯϓϧʣͱͯ͠29Direction (deg)

0.5

0.45

0.4

正答率

0.35

Time (sec)

0.3

ਤ 7: σʔληοτ (B) ͷΠϕϯτɽϥϕϧ͸ද 2 ͱରԠɽ

0.25
0.8

0.2

SCBPM

0.75

カスケード法

0.15

0.7

0.1
0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9
0.65

正答率

アノテーション率ਤ 5: σʔληοτ (A) ʹؔ͢Δਖ਼౴཰ͷൺֱ

0.6

0.55

Direction (deg)

0.5

SCBPM
0.45

カスケード法0.40.1ϑϨʔϜͷεςοϓ෯Ͱ 100 ϑϨʔϜ෼Λ 1 ϒϩοΫ 4100
࣍‫ݩ‬ͷϕΫτϧͱΈͳ͠ɼPCA ʹΑΓ 32 ࣍‫ݩ࣍ͱ΁ݩ‬࡟‫͠ݮ‬ɼར༻ͨ͠ɽ·ͨɼ͜ͷ 1 ϒϩοΫ͝ͱʹΫϥεΛ
ਪఆ͠ɼ࠷ऴతʹΠϕϯτ಺ͷશͯͷϒϩοΫͷଟ਺ܾʹΑͬͯͦͷΠϕϯτͷΫϥεΛܾఆͨ͠ɽGMM ͷࠞ߹਺͸ 30 ͱͨ͠ɽ൒‫͋ࢣڭ‬ΓֶशͰ͸ϥϕϧ͋ΓσʔλͷॏΈΛͱϥϕϧͳ͠σʔλͷॏΈΛઃఆͨ͠ [Nigam 00].
ϥϕϧ͋ΓσʔλͷॏΈΛ 1.0 ͱ͠ɼϥϕϧͳ͠σʔλͷॏΈ͸σʔληοτ (A) ʹؔͯ͠͸ 0.1ɼσʔληοτ(B) ʹؔͯ͠͸ 0.001 ͱͨ͠ɽ
ਤ 5 ͱ ਤ 8 ͔Β͜ͷਤ͔ΒશͯͷΞϊςʔγϣϯ཰ʹ͓͍ͯɼSCBPM ͕ྑ͍ਖ਼౴཰Ͱ͋Δ͜ͱ͕Θ͔Δɽ·ͨɼσʔληοτ (B) ʹൺ΂σʔληοτ (A) ͷਖ਼౴཰͕Χεέʔυ๏ɼSCBPM ͱ΋ʹ௿͍͜ͱ͕Θ͔Δɽ͜Ε͸σʔληοτ (A) ͷ΄͏͕ಉ࣌ʹՎ͍ͬͯΔௗͷ਺͕ଟ͘ʢਤ 6ʣɼ‫׬‬શͳ෼཭͕ࠔ೉Ͱ͋ͬͨͨΊͱߟ͑ΒΕΔɽఏҊ๏͸ɼಛʹɼ͜ͷΑ͏ͳ෼཭ͷੑೳ͕௿͍৔߹ʹ͓͍ͯɼൺֱతྑ͍ੑೳͰ͋Γɼ࠷େͰ͓Αͦ 5%ͷ͕ࠩ֬ೝͰ͖ͨɽҰํɼσʔληοτ (B) Ͱ͸ಉ࣌ʹՎ͍ͬͯΔௗͷ਺͕গͳ͘ʢਤ 7ʣɼ͍͔ͭ͘ͷΠϕϯτʹ͍ͭͯ͸ଞͷௗͷํ޲ͱॏෳͯ͠͠·͍ͬͯΔɽ͜ͷΑ͏ͳঢ়‫Ͱگ‬͸ఏҊ๏͕͋·ΓΑ͘ಇ͔ͳ͍͕ɼͦͷΑ͏ͳ৔߹
3 ͜ΕΒͷύϥϝʔλ͸ਓ͕ؒɽௗͷՎΛࣝผ͢ΔࡍʹΑ͘ར༻͢ΔύϥϝʔληοτͷҰͭͱಉ͡΋ͷΛ༻͍ͨɽ

0.30.40.50.60.70.80.9

ਤ 8: σʔληοτ (B) ʹؔ͢Δਖ਼౴཰ͷൺֱਤ 6: σʔληοτ (A) ͷΠϕϯτɽϥϕϧ͸ද 1 ͱରԠɽSTFT ʹΑΓप೾਺εϖΫτϧΛ‫ ͨ͠ࢉܭ‬3 ɽ͞Βʹɼ100.2

アノテーション率Time (sec)Ͱ΋ैདྷ๏ͱಉ౳ͷੑೳ͕֬ೝͰ͖ͨɽ

7·ͱΊఆҐ৘ใΛར༻ͨ͠Ի‫ݯ‬ಉఆϞσϧͰ͋Δ SCBPM ΛఏҊ͠ɼϞσϧύϥϝʔλΛ෼཭ԻؒͷґଘੑΛߟྀͭ͠
ֶͭश͢Δ EM ΞϧΰϦζϜΛಋೖͨ͠ɽ·ͨɼͦͷϞσϧΛ༻͍ͯԻ‫ݕݯ‬ग़ɾఆҐɾ෼཭ɾಉఆΛߦ͏ΞϊςʔγϣϯγεςϜͷϓϩτλΠϓΛߏங͠ɼ࣮ࡍͷϑΟʔϧυʹ͓͚ΔௗͷՎͷσʔλΛ༻͍ͯఏҊ๏ͷ༗ޮੑΛࣔͨ͠ɽࠓ‫ޙ‬͸ɼΑΓେ‫ن‬໛ͳσʔλΛ༻͍ͨύϥϝʔλֶशΛߦ͍ɼԻ‫ݯ‬ಉఆͷਖ਼౴཰Λ্ঢͤ͞Δ͜ͱ΍ຊγεςϜͷεέʔϥϏϦςΟΛධՁ͢Δ͜ͱ͕՝୊Ͱ͋Δɽ·ͨɼઐ໳ՈʹௗͷՎͷ෼ੳγεςϜΛར༻ͯ͠΋Β͍ɼධՁΛಘΔ͜ͱͰ৽ͨͳ՝୊ͷநग़Λߦ͍͍ͨͱߟ͍͑ͯΔɽँࣙຊ‫͚͓ʹڀݚ‬ΔϑΟʔϧυͰͷऩ࿥खॱ΍‫ݱ‬ঢ়ʹ͍ͭͯॿ‫ݴ‬Λ௖໊͍ͨ‫ݹ‬԰େֶͷদྛࢤอࢯʹ‫͢ँײ‬Δɽຊ ‫ ڀ ݚ‬͸ ɺJSPS Պ ‫ ݚ‬අ 24220006, 16H02884,16K00294 ͓ΑͼɼJST ImPACT λϑϩϘςΟΫενϟϨϯδͷॿ੒Λ͏͚ͨɽࢀߟจ‫ݙ‬[Banerjee 05] Banerjee, A., Dhillon, I. S., Ghosh, J., and Sra, S.:
Clustering on the unit hypersphere using von Mises-Fisher distributions, Journal of Machine Learning Research, Vol. 6, No.
Sep, pp. 1345–1382 (2005)

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Sound Source Separation and Deep Learning with a QuadrotorEmbedded Microphone Array, in ICAM2015, pp. 329–330,
JSME (2015)ද 2: σʔληοτ (B): ϥϕϧͱΠϕϯτͷ਺ɽ৭͸ਤ7 ͱରԠɽϥϕϧΠϕϯτ਺৭

Paciﬁc-Slope Flycatcher
Spotted Towhee
Nashville Warbler

7812greenredblueBlack-Headed Grosbeak
Orange-Crowned Warbler

104cyanyellow

Cassin’s Vireo
Unknown bird song
Others90
63magenta
dark green
dark red[খౡ 15] খౡ ྒհ, ਿࢁ ࣏, ླ໦ ྷ࣑, தᢕ ҰതɿԻ‫ݯ‬ΞϊςʔγϣϯิॿͷͨΊͷԻ‫ݯ‬ҐஔΛߟྀͨ͠ಉఆϞσϧ, in RSJ2015, ೔ຊϩϘοτֶձ (2015)[খౡ 16] খౡ ྒհ, ਿࢁ ࣏, ླ໦ ྷ࣑, தᢕ ҰതɿԻ‫ݯ‬ҐஔΛߟྀͨ͠Ի‫ݯ‬ಉఆͷͨΊͷ֬཰Ϟσϧͱͦͷֶश, in RSJ2016, ೔ຊϩϘοτֶձ (2016)[Ԟ೫ 10] Ԟ೫ ത, தᢕ ҰതɿϩϘοτௌ֮Φʔϓϯιʔειϑτ΢ΤΞ HARK, ೔ຊϩϘοτֶձࢽ, Vol. 28, No. 1, pp. 6–9 (2010)[দྛ 15] দྛ ࢤอ, ླ໦ ྷ࣑, খౡ ྒհ, தᢕ Ұതɿෳ਺ͷϚΠΫϩϑΥϯΞϨΠͱϩϘοτௌ֮ιϑτ΢ΣΞ HARK Λ༻͍ͨ໺ௗͷҐஔ‫؍‬ଌਫ਼౓ͷ‫ݕ‬౼, ୈ 43 ճ AI-Challenge ‫ڀݚ‬ձ, pp. 54–59, ਓ޻஌ೳֶձ (2015)[Briggs 13] Briggs, F., Raich, R., Eftaxias, K., Lei, Z., andHuang, Y.: The ninth annual MLSP competition: overview,in IEEE International workshop on machine learning for signal processing, Southampton, United Kingdom., Sept, pp. 22–25(2013)[Catchpole 03] Catchpole, C. K. and Slater, P. J.: Bird song:
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