Abstract: The transcription of music is essential since it preserves the originality of the music and the compositional technique used by the composers. In this manner, native music can be reproduced, and documents of certain tunes can be passed on to succeeding generations without hearing the original music. A very limited amount of research has been conducted on the application of automation in music transcription using deep learning, particularly in native music instruments. This research is an effort to preserve and conserve Filipino culture in the context of native music and musical instruments particularly the Diwdiw-as, a native Filipino bamboo flute. Using signal processing and deep neural networks, the proposed study aims to automate music transcription. The system is capable of classifying pitches based on the fundamental frequency and is also capable of classifying notes based on their duration. Diwdiw-as pitch can be transcribable to the music sheet using the following pitches: CS, DS, ES, FS, GS, AS, BS, C6, while the note values are whole, eighth, quarter, half, dotted eighth, dotted half, dotted quarter. A web-based application has been developed to assist in the automatic music transcription (AMT) of Diwdiw-as. A pdf file of the transcribed music sheet can then be downloaded. According to the confusion matrices, the system's accuracy is high in terms of the transcription of pitches and notes in the music sheet.