hop_length = 256
win_length = 2048
f_novfn = lambda x, sr: get_novfn(x, sr, hop_length, win_length)
results = evaluate_tempos(f_novfn, get_fourier_tempo, hop_length, 44100)
ipd.HTML(results.to_html(escape=False, float_format='%.2f'))

5 / 20

	names	Ground-Truth Tempos	Estimated Tempos	Close Enough
0	train1.wav	[64.5, 129.5]	258.05	False
1	train2.wav	[83.5, 167.5]	8.00	False
2	train3.wav	[76.5, 153.0]	306.06	False
3	train4.wav	[42.0, 126.0]	126.00	True
4	train5.wav	[68.5, 205.5]	206.04	True
5	train6.wav	[41.0, 82.0]	164.03	False
6	train7.wav	[56.5, 113.5]	56.01	True
7	train8.wav	[74.0, 148.0]	592.00	False
8	train9.wav	[64.5, 129.0]	258.00	False
9	train10.wav	[61.0, 122.5]	2.00	False
10	train11.wav	[70.0, 140.0]	280.05	False
11	train12.wav	[27.0, 54.0]	2.00	False
12	train13.wav	[90.0, 180.0]	360.07	False
13	train14.wav	[65.0, 130.0]	520.10	False
14	train15.wav	[62.0, 186.0]	186.03	True
15	train16.wav	[45.0, 90.5]	11.98	False
16	train17.wav	[45.5, 91.5]	12.00	False
17	train18.wav	[61.0, 121.5]	2.00	False
18	train19.wav	[93.5, 188.0]	188.04	True
19	train20.wav	[115.5, 220.5]	440.08	False